Research Article
BibTex RIS Cite

Türk imalat sistemlerinde iş kazalarındaki örüntülerin çok durumlu logit model’e dayalı bir yaklaşımla belirlenmesi

Year 2024, Volume: 39 Issue: 2, 1049 - 1066, 30.11.2023
https://doi.org/10.17341/gazimmfd.1131524

Abstract

Türk imalat sanayi sektörler arasında iş kazası sıklığı açısından ilk üç içinde yer almaktadır. Bu nedenle imalat sanayinde iş güvenliğinin artırılması ve iş kazalarına neden olan risklerin en aza indirilmesi için kaza neden-sonuç ilişkilerinin belirlenmesine ihtiyaç vardır. Bu çalışmada Türk imalat sistemlerindeki iş kazaları arasındaki örüntüleri bulmak için entegre bir veri odaklı yaklaşım önerilmiştir. Önerilen yaklaşım, C5.0, Sınıflandırma ve regresyon ağaçları (C&RT), Kuaterniyon tahmini (QUEST), Ki-kare otomatik etkileşim dedektörü (CHAID) ve Rastgele ağaçlar (Random Forest) olmak üzere karar ağacı algoritmalarını ve çok terimli logit modeli kullanmaktadır. Bu çalışmada 2013-2019 yılları arasında Türk imalat sanayinde meydana gelen 307.590 iş kazası kullanılmıştır. Yaralanma, ölüm ve uzuv kaybı olan tüm kazalar için sektör bölümü, kazanın yaşandığı coğrafi bölge, yıl, sapma, saat gün, cinsiyet ve yaş arasında iş göremezlik durumuna göre istatistiksel olarak anlamlı bir ilişki olduğu bulunmuştur. Ek olarak, sektör bölümü, kazanın yaşandığı coğrafi bölge ve yıl, karar ağacı algoritmalarına dayalı ilk beş tahmin edici arasında bulunmuştur.

References

  • [1]. Eurostat. NACE Rev. 2 Statistical classification of economic activities in the European Community. Official Publications of the European Communities, https://ec.europa.eu/eurostat/documents/3859598/5902521/KS-RA-07-015-EN.PDF. 112-285. Yayın tarihi 2008. Erişim Tarihi Mayıs 20, 2020.
  • [2]. Min, J., Kim, Y., Lee, S., Jang, T. W., Kim, I., & Song, J., The fourth industrial revolution and its impact on occupational health and safety, worker's compensation and labor conditions, Safety and Health at Work, 10(4), 400-408, 2019.
  • [3]. Felknor, S. A., Streit, J. M., McDaniel, M., Schulte, P. A., Chosewood, L. C., & Delclos, G. L., How Will the Future of Work Shape OSH Research and Practice? A Workshop Summary, International Journal of Environmental Research and Public Health, 18(11), 5696, 2022.
  • [4]. T.C. SGK İstatistik Yıllığı, 2013, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2014. Erişim Tarihi Temmuz 5, 2020.
  • [5]. T.C. SGK İstatistik Yıllığı, 2014, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2015. Erişim Tarihi Temmuz 5, 2020.
  • [6]. T.C. SGK İstatistik Yıllığı, 2015, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2016. Erişim Tarihi Temmuz 5, 2020.
  • [7]. T.C. SGK İstatistik Yıllığı, 2016, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2017. Erişim Tarihi Temmuz 5, 2020.
  • [8]. T.C. SGK İstatistik Yıllığı, 2017, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2018. Erişim Tarihi Temmuz 5, 2020.
  • [9]. T.C. SGK İstatistik Yıllığı, 2018, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2019. Erişim Tarihi Temmuz 5, 2020.
  • [10]. T.C. SGK İstatistik Yıllığı, 2019, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2020. Erişim Tarihi Temmuz 5, 2020.
  • [11]. T.C. SGK İstatistik Yıllığı, 2020, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2021. Erişim Tarihi Eylül 6, 2021.
  • [12]. Tunji-Olayeni, P. F., Afolabi, A. O., Olowookere, E. I., Okpalamoka, O. I., & Oluwatobi, A. O., Implications of occupational hazards on attainment of the Sustainable Development Goals in the Nigerian Construction Industry, In IOP Conference Series: Materials Science and Engineering, Kazimierz Dolny-Poland, 640(1), 012129, 2019. [13]. Hasle, P., & Vang, J., Designing better interventions: insights from research on decent work, Journal of Supply Chain Management, 57(2), 58-70, 2021.
  • [14]. Kazanin, O. I., Rudakov, M. L., & Kolvakh, K. A., Occupational safety and health in the sector of coal mining, International Journal of Civil Engineering and Technology, 9(6), 1333-1339, 2018.
  • [15]. Stemn, E., Ntsiful, F., Azadah, M. A., & Joe-Asare, T., Incident causal factors and the reasons for conducting investigations: a study of five ghanaian large-scale mines, Safety, 6(1), 9, 2020.
  • [16]. Lombardi, M., Fargnoli, M., & Parise, G., Risk profiling from the european statistics on accidents at work (ESAW) accidents′ databases: A case study in construction sites, International journal of environmental research and public health, 16(23), 4748, 2019.
  • [17]. Goh, Y. M., & Ubeynarayana, C. U., Construction accident narrative classification: An evaluation of text mining techniques, Accident Analysis & Prevention, 108, 122-130, 2017.
  • [18]. Altunkaynak, B., A statistical study of occupational accidents in the manufacturing industry in Turkey, International journal of industrial ergonomics, 66, 101-109, 2018.
  • [19]. Oduoza, C. F., Framework for sustainable risk management in the manufacturing sector, Procedia Manufacturing, 51, 1290-1297, 2020.
  • [20]. Eskandari, D., Charkhand, H., Jafari, M. J., Pouyakian, M., Torshizi, Y. F., & Mehrabi, Y., Development of a leading indicator for assessing the organization's safety performance based on fuzzy AHP, Iran Occupational Health, 17(53), 2020.
  • [21]. Alkan, G., Farrow, R., Liu, H., Moore, C., Ng, H. K. T., Stokes, L., ... & Zhong, Y., Predictive modeling of maximum injury severity and potential economic cost in a car accident based on the General Estimates System data, Computational Statistics, 36(3), 1561-1575, 2021.
  • [22]. Alicioglu, G., Sun, B., & Ho, S. S., Assessing Accident Risk using Ordinal Regression and Multinomial Logistic Regression Data Generation, In 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow-UK, 1-8, 2020.
  • [23]. Zhou, B., Wang, X., Zhang, S., Li, Z., Sun, S., Shu, K., & Sun, Q., Comparing factors affecting injury severity of passenger car and truck drivers, IEEE Access, 8, 153849-153861, 2020.
  • [24]. Rezapour, M., Molan, A. M., & Ksaibati, K., Application of multinomial regression model to identify parameters impacting traffic barrier crash severity, The Open Transportation Journal, 13(1), 2019.
  • [25]. Aljarrah, M. F., Khasawneh, M. A., & Al-Omari, A. A., Investigating Key Factors Influencing the Severity of Drivers Injuries in Car Crashes Using Supervised Machine Learning Techniques, Journal of Engineering Science & Technology Review, 12(4), 2019.
  • [26]. Sarkar, S., Raj, R., Vinay, S., Maiti, J., & Pratihar, D. K., An optimization-based decision tree approach for predicting slip-trip-fall accidents at work, Safety science, 118, 57-69, 2019.
  • [27]. Basha, S. A., & Maiti, J., Relationships of demographic factors, job risk perception and work injury in a steel plant in India, Safety science, 51(1), 374-381, 2013.
  • [28]. Park, R. M., Ahn, Y. S., Stayner, L. T., Kang, S. K., & Jang, J. K., Mortality of iron and steel workers in Korea, American journal of industrial medicine, 48(3), 194-204, 2005.
  • [29]. Konstandinidou, M., Nivolianitou, Z., Kefalogianni, E., & Caroni, C., In-depth analysis of the causal factors of incidents reported in the Greek petrochemical industry, Reliability Engineering & System Safety, 96(11), 1448-1455, 2011.
  • [30]. Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X., Applied logistic regression, 398. John Wiley & Sons, 2013.
  • [31]. Kwak, C., & Clayton-Matthews, A., Multinomial logistic regression, Nursing research, 51(6), 404-410, 2002.
  • [32]. Güyagüler, T., & Bozkurt, R., Kömür Madenciliğinde Meydana Gelen İş Kazalarının. Maliyetleri, Türkiye, 8, 331-343, 1992.
  • [33]. Collie, A., Simpson, P. M., Cameron, P. A., Ameratunga, S., Ponsford, J., Lyons, R. A., ... & Gabbe, B. J., Patterns and predictors of return to work after major trauma: a prospective, population-based registry study, Annals of surgery, 269(5), 972-978, 2019.
  • [34]. Sze, N. N., & Song, Z., Factors contributing to injury severity in work zone related crashes in New Zealand, International journal of sustainable transportation, 13(2), 148-154, 2019.
  • [35]. Tait, R. C., & Chibnall, J. T., Workers’ compensation claimants with low back pain: the role of dissatisfaction in the transition to disability, Psychological injury and law, 9(1), 16-22, 2016.
  • [36]. Palei, S. K., Karmakar, N. C., & Reddy, R. S., Effects of demography and occupational traits on consequence of injury of underground coal miners, In 2014 IEEE International Conference on Industrial Engineering and Engineering Management, Selangor-Malaysia, 1260-1264, 2014.
  • [37]. Gholizadeh, P., & Esmaeili, B., Developing a multi-variate logistic regression model to analyze accident scenarios: Case of electrical contractors, International journal of environmental research and public health, 17(13), 4852, 2020.
  • [38]. Lee, W., Lee, J., Kim, U. J., Yoon, J. H., Choi, W. J., Ham, S., ... & Kang, S. K., Working conditions and mental health status related with occupational injury of Korean outdoor workers, Journal of occupational and environmental medicine, 62(7), e334-e339, 2020.
  • [39]. Fabiano, B., Currò, F., Reverberi, A. P., & Pastorino, R., A statistical study on temporary work and occupational accidents: specific risk factors and risk management strategies, Safety science, 46(3), 535-544, 2008.
  • [40]. Gholizadeh, P., Onuchukwu, I. S., & Esmaeili, B., Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013, International Journal of Environmental Research and Public Health, 18(10), 5126, 2021.
  • [41]. Mosquera, R., Parra, L., Ledesma, A. J., & Bonilla, H. F., Predicción de la accidentalidad laboral en la industria de pulpa y papel usando algoritmos de clasificación, Información tecnológica, 32(1), 133-142, 2021.
  • [42]. Sarkar, S., Vinay, S., Raj, R., Maiti, J., & Mitra, P., Application of optimized machine learning techniques for prediction of occupational accidents, Computers & Operations Research, 106, 210-224, 2019.
  • [43]. Şahin, D. Ö., Şirin, B., Akleylek, S., & Kılıç, E., Work Accident Analysis with Machine Learning Techniques, In 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo- Bosnia Herzegovina, 215-219, 2018.
  • [44]. Shirali, G. A., Noroozi, M. V., & Malehi, A. S., Predicting the outcome of occupational accidents by CART and CHAID methods at a steel factory in Iran, Journal of public health research, 7(2), 74-80, 2018.
  • [45]. Sarkar, S., Pateshwari, V., & Maiti, J., Predictive model for incident occurrences in steel plant in India, In 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi-India, 1-5, 2017.
  • [46]. Comberti, L., Baldissone, G., & Demichela, M., Workplace accidents analysis with a coupled clustering methods: Som and k-means algorithms, Chemical Engineering Transactions, 43, 1261-1266, 2015.
  • [47]. Sanmiquel, L., Rossell, J. M., & Vintró, C., Study of Spanish mining accidents using data mining techniques, Safety science, 75, 49-55, 2015.
  • [48]. Cheng, C. W., Yao, H. Q., & Wu, T. C., Applying data mining techniques to analyze the causes of major occupational accidents in the petrochemical industry, Journal of Loss Prevention in the Process Industries, 26(6), 1269-1278, 2013.
  • [49]. Cheng, C. W., Leu, S. S., Cheng, Y. M., Wu, T. C., & Lin, C. C., Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan's construction industry, Accident Analysis & Prevention, 48, 214-222, 2012.
  • [50]. Reyes-Martínez, R. M., Maldonado-Macías, A., & Prado-León, L. R., Human factors identification and classification related to accidents’ causality on hand injuries in the manufacturing industry, Work, 3155-3163, 2012.
  • [51]. Ciarapica, F. E., & Giacchetta, G., Classification and prediction of occupational injury risk using soft computing techniques: An Italian study, Safety science, 47(1), 36-49, 2009.
  • [52]. Bevilacqua, M., Ciarapica, F. E., & Giacchetta, G., Industrial and occupational ergonomics in the petrochemical process industry: A regression trees approach, Accident Analysis & Prevention, 40(4), 1468-1479, 2008.
  • [53]. Liao, C. W., Perng, Y. H., & Chiang, T. L., Discovery of unapparent association rules based on extracted probability, Decision Support Systems, 47(4), 354-363, 2009.
  • [54]. McFadden, D., Conditional logit analysis of qualitative choice behavior. In Frontiers in Econometrics, edited by P. Zarembka, Academic Press, New York, 1974.
  • [55]. Şengül, Z., Ege Bölgesinde Arıcılık Yapan İşletmelerin Sürdürülebilirlik Yönünden Değerlendirilmesi, Doktora Tezi, Fen Bilimleri Enstitütsü, Ege Üniversitesi, İzmir, 2020.
  • [56]. El-Habil, A. M., An application on multinomial logistic regression model. Pakistan journal of statistics and operation research, 271-291, 2012.
  • [57]. Katz, M. H., Multivariable analysis: a practical guide for clinicians and public health researchers, Cambridge university press, 2011.
  • [58]. Liao, T. F. F., & Liao, T. F., Interpreting probability models: Logit, probit, and other generalized linear models, 101, Sage, 1994.
  • [59]. Goplerud, M., A Multinomial Framework for Ideal Point Estimation, Political Analysis, 27(1), 69-89, 2019.
  • [60]. Erkan, A. R. I., Using multinomial logistic regression to examine the relationship between children’s work status and demographic characteristics, Siyaset, Ekonomi ve Yönetim Araştırmaları Dergisi, 4(1), 77-93, 2016.
  • [61]. Chen, Z., & Fan, W. D., A multinomial logit model of pedestrian-vehicle crash severity in North Carolina, International journal of transportation science and technology, 8(1), 43-52, 2019.
  • [62]. Izadi, N., Aminian, O., & Esmaeili, B., Occupational Accidents in Iran: Risk Factors and Long Term Trend (2007–2016), Journal of research in health sciences, 19(2), e00448, 2019.
  • [63]. Al-Behadili, H. N. K., & Ku-Mahamud, K. R., Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification, Journal of Information and Communication Technology, 20(3), 391-422, 2021.
  • [64]. Revathy, G., Kumar, P. S., & Rajendran, V., Development of IDS using mining and machine learning techniques to estimate DoS malware, International Journal of Computational Science and Engineering, 24(3), 259-275, 2021.
  • [65]. Khalifa, R. M., Yacout, S., & Bassetto, S. 2021, Developing machine-learning regression model with Logical Analysis of Data (LAD), Computers & Industrial Engineering, 151, 106947, 2021.
  • [66]. Rokach, L., Romano, R., & Maimon, O., Mining manufacturing databases to discover the effect of operation sequence on the product quality, Journal of Intelligent manufacturing, 19(3), 313-325, 2008.
  • [67]. Stallard, T., & Levitt, K., Automated analysis for digital forensic science: Semantic integrity checking, In 19th Annual Computer Security Applications Conference, DC-United States, 160-167, 2003.
  • [68]. Hur, J., Lee, H., & Baek, J. G., An intelligent manufacturing process diagnosis system using hybrid data mining, In Industrial Conference on Data Mining, Berlin- Heidelberg, 561-575, 2006.
  • [69]. Sadic, S., & Kayakutlu, G., Integrating decision trees and cognitive maps for market segmentation in service sector, In PICMET'07-2007 Portland International Conference on Management of Engineering & Technology, Portland- OR USA 2748-2754, 2007.
  • [70]. Lin, J., A web forensic system based on semantic checking, In 2008 International Symposium on Computational Intelligence and Design, Wuhan, 1, 99-102, 2008.
  • [71]. Jun, L. I. U., Algorithm for decision tree construction for incompatible table, Journal of Hohai University (Natural Sciences), 2, 2013.
  • [72]. Gupta, P., Mehrotra, D., & Sharma, T. K., Role of decision tree in supplementing tacit knowledge for Hypothetico-Deduction in higher Education, International Journal of System Assurance Engineering and Management, 9(1), 82-90, 2018.
  • [73]. Qiong-sheng, Z., Ming-quan, W., Tong-xuan, L., & Xiao-wei, C., An Algorithm for Decision Tree Construction Based on Degree of Rough Classification, In 2010 International Conference on Artificial Intelligence and Computational Intelligence, Washington-DC United States 3, 23-29, 2010.
  • [74]. Xun, Y., Yin, Q., Zhang, J., Yang, H., & Cui, X., A novel discretization algorithm based on multi-scale and information entropy, Applied Intelligence, 51(2), 991-1009, 2021. [75]. Uncu, I. S., & Kayakus, M., Determınation of Effıcıent Light Source For Wheat Plant By Using Decision Trees Method, Fresenius Environmental Bulletin, 30(3), 2807-2812, 2021.
  • [76]. Horng, S. C., Yang, F. Y., & Lin, S. S., Hierarchical fuzzy clustering decision tree for classifying recipes of ion implanter, Expert Systems with Applications, 38(1), 933-940, 2011.
  • [77]. Pei, D., Gong, Y., Kang, H., Zhang, C., & Guo, Q., Accurate and rapid screening model for potential diabetes mellitus, BMC medical informatics and decision making, 19(1), 1-8, 2019.
  • [78]. Yang, T., Zhao, B., & Pei, D., Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach, Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 14, 3437, 2021.
  • [79]. Chawla, N. V., Bowyer, K. W., Hall, L. O., & KEgelmeyer, W. P., SMOTE: synthetic minority over-sampling technique, Journal of artificial intelligence research, 16, 321-357, 2002. [80]. Jiawei, H., & Kamber, M., Data mining: conceptsand techniques. M, SanFrancisco, Morgan Kaufman Publish—ers, 10-120, 2001.
  • [81]. Witten, D. M., Classification and clustering of sequencing data using a Poisson model, The Annals of Applied Statistics, 5(4), 2493-2518, 2011.
  • [82]. Maimon, O., & Rokach, L. (Eds.), Data mining and knowledge discovery handbook, 2005.
  • [83]. Buckland, M., & Gey, F., The relationship between recall and precision, Journal of the American society for information science, 45(1), 12-19, 1994.
  • [84]. Coşkun, C., & Baykal, A., Veri madenciliğinde sınıflandırma algoritmalarının bir örnek üzerinde karşılaştırılması, Akademik Bilişim, 2011, 1-8, 2011.
  • [85]. Mutlu, N. G., & Altuntas, S., Assessment of occupational risks In Turkish manufacturing systems with data-driven models, Journal of Manufacturing Systems, 53, 169-182, 2019.
  • [86]. Köse, N., & Ersöz, F., Veri Madenciliğinde Karar Ağacı Algoritmaları İle Demir Çelik Endüstrisinde İş Kazaları Üzerine Bir Uygulama, Avrupa Bilim ve Teknoloji Dergisi, 397-407, 2020.
  • [87]. Hajakbari, M. S., & Minaei-Bidgoli, B., A new scoring system for assessing the risk of occupational accidents: A case study using data mining techniques with Iran's Ministry of Labor data, Journal of Loss Prevention in the Process Industries, 32, 443-453, 2014.
  • [88]. Declaration on Occupational Health and Safety in Workplace Hazard Classes, No: 28509. https://www.resmigazete.gov.tr/eskiler/2012/12/20121226-11.htm, Yayın tarihi 2012. Erişim Tarihi Ekim 28, 2021.
  • [89]. Hausman, J. ve McFadden, D., Spesification Tests for the Multinomial Logit Model, Econometrica, 52, 1219-1240, 1984.
  • [90]. Farahbod, H., Ghiyasi, S., & Soltanzadeh, A., Association of Non-Organizational Factors and Occupational Accidents: A Field Study based on Structural Equation Modeling. Journal of Occupational Health and Epidemiology, 10(1), 31-38, 2021.
  • [91]. Kurt Gök, D., Ünal, İ., & Aslan-Kara, K., Evaluation of the effects of shift work on parasomnia prevalence, Chronobiology International, 38(10), 1500-1506, 2021.
  • [92]. Rajaratnam, S. M., Howard, M. E., & Grunstein, R. R., Sleep loss and circadian disruption in shift work: health burden and management, Medical Journal of Australia, 199, S11-S15, 2013.
  • [93]. Szóstak, M., Analysis of occupational accidents in the construction industry with regards to selected time parameters, Open Engineering, 9(1), 312-320, 2019.
  • [94]. Pietilä, J., Räsänen, T., Reiman, A., Ratilainen, H., & Helander, E., Characteristics and determinants of recurrent occupational accidents, Safety science, 108, 269-277, 2018.
  • [95]. Allman, M., Allmanová, Z., & Jankovský, M., Is cable yarding a dangerous occupation? A Survey from the public and private sector, Central European Forestry Journal, 64(2), 127-132, 2018.
  • [96]. de Medeiros Prudêncio, A. L., Marques, B. G., Aguiar, D. R., Lima, L. C., Cabral, L. D., Quadros, R. W., & Magajewski, F. R., Socioeconomic and demographic profile of occupational morbidity and mortality in Brazil from 2009 to 2016, Revista brasileira de medicina do trabalho, 19(1), 68, 2021.
  • [97]. Rembiasz, M., Impact of employee age on the safe performance of production tasks. In MATEC Web of Conferences, 94, 07009, 2017.
  • [98]. Yi, K. H., & Lee, S. S., A policy intervention study to identify high-risk groups to prevent industrial accidents in Republic of Korea, Safety and health at work, 7(3), 213-217, 2016.
  • [99]. Nola, A., Cattaneo, G., Maiocchi, A., Gariboldi, C., Rocchi, R., Cavallaro, S., ... & Bassino, P., Occupational accidents in temporary work, La Medicina Del Lavoro, 92(4), 281-285, 2001.
  • [100]. Nodoushan, R. J., Akhavan, A., Miyanshahri, M. E., & Anoosheh, V. S., Investigation of the relationship between occupational cognitive failures and work-related accidents in heavy equipment operators of Shahid Rajaee port complex, Journal of Education and Health Promotion, 9(1), 189, 2020.
  • [101]. Abdar, M., A survey and compare the performance of IBM SPSS modeler and rapid miner software for predicting liver disease by using various data mining algorithms, Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, 36(3), 3230-3241, 2015.
Year 2024, Volume: 39 Issue: 2, 1049 - 1066, 30.11.2023
https://doi.org/10.17341/gazimmfd.1131524

Abstract

References

  • [1]. Eurostat. NACE Rev. 2 Statistical classification of economic activities in the European Community. Official Publications of the European Communities, https://ec.europa.eu/eurostat/documents/3859598/5902521/KS-RA-07-015-EN.PDF. 112-285. Yayın tarihi 2008. Erişim Tarihi Mayıs 20, 2020.
  • [2]. Min, J., Kim, Y., Lee, S., Jang, T. W., Kim, I., & Song, J., The fourth industrial revolution and its impact on occupational health and safety, worker's compensation and labor conditions, Safety and Health at Work, 10(4), 400-408, 2019.
  • [3]. Felknor, S. A., Streit, J. M., McDaniel, M., Schulte, P. A., Chosewood, L. C., & Delclos, G. L., How Will the Future of Work Shape OSH Research and Practice? A Workshop Summary, International Journal of Environmental Research and Public Health, 18(11), 5696, 2022.
  • [4]. T.C. SGK İstatistik Yıllığı, 2013, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2014. Erişim Tarihi Temmuz 5, 2020.
  • [5]. T.C. SGK İstatistik Yıllığı, 2014, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2015. Erişim Tarihi Temmuz 5, 2020.
  • [6]. T.C. SGK İstatistik Yıllığı, 2015, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2016. Erişim Tarihi Temmuz 5, 2020.
  • [7]. T.C. SGK İstatistik Yıllığı, 2016, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2017. Erişim Tarihi Temmuz 5, 2020.
  • [8]. T.C. SGK İstatistik Yıllığı, 2017, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2018. Erişim Tarihi Temmuz 5, 2020.
  • [9]. T.C. SGK İstatistik Yıllığı, 2018, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2019. Erişim Tarihi Temmuz 5, 2020.
  • [10]. T.C. SGK İstatistik Yıllığı, 2019, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2020. Erişim Tarihi Temmuz 5, 2020.
  • [11]. T.C. SGK İstatistik Yıllığı, 2020, http://www.sgk.gov.tr/wps/portal/sgk/tr/kurumsal/istatistik/sgk_istatistik_yilliklari, Yayın tarihi 2021. Erişim Tarihi Eylül 6, 2021.
  • [12]. Tunji-Olayeni, P. F., Afolabi, A. O., Olowookere, E. I., Okpalamoka, O. I., & Oluwatobi, A. O., Implications of occupational hazards on attainment of the Sustainable Development Goals in the Nigerian Construction Industry, In IOP Conference Series: Materials Science and Engineering, Kazimierz Dolny-Poland, 640(1), 012129, 2019. [13]. Hasle, P., & Vang, J., Designing better interventions: insights from research on decent work, Journal of Supply Chain Management, 57(2), 58-70, 2021.
  • [14]. Kazanin, O. I., Rudakov, M. L., & Kolvakh, K. A., Occupational safety and health in the sector of coal mining, International Journal of Civil Engineering and Technology, 9(6), 1333-1339, 2018.
  • [15]. Stemn, E., Ntsiful, F., Azadah, M. A., & Joe-Asare, T., Incident causal factors and the reasons for conducting investigations: a study of five ghanaian large-scale mines, Safety, 6(1), 9, 2020.
  • [16]. Lombardi, M., Fargnoli, M., & Parise, G., Risk profiling from the european statistics on accidents at work (ESAW) accidents′ databases: A case study in construction sites, International journal of environmental research and public health, 16(23), 4748, 2019.
  • [17]. Goh, Y. M., & Ubeynarayana, C. U., Construction accident narrative classification: An evaluation of text mining techniques, Accident Analysis & Prevention, 108, 122-130, 2017.
  • [18]. Altunkaynak, B., A statistical study of occupational accidents in the manufacturing industry in Turkey, International journal of industrial ergonomics, 66, 101-109, 2018.
  • [19]. Oduoza, C. F., Framework for sustainable risk management in the manufacturing sector, Procedia Manufacturing, 51, 1290-1297, 2020.
  • [20]. Eskandari, D., Charkhand, H., Jafari, M. J., Pouyakian, M., Torshizi, Y. F., & Mehrabi, Y., Development of a leading indicator for assessing the organization's safety performance based on fuzzy AHP, Iran Occupational Health, 17(53), 2020.
  • [21]. Alkan, G., Farrow, R., Liu, H., Moore, C., Ng, H. K. T., Stokes, L., ... & Zhong, Y., Predictive modeling of maximum injury severity and potential economic cost in a car accident based on the General Estimates System data, Computational Statistics, 36(3), 1561-1575, 2021.
  • [22]. Alicioglu, G., Sun, B., & Ho, S. S., Assessing Accident Risk using Ordinal Regression and Multinomial Logistic Regression Data Generation, In 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow-UK, 1-8, 2020.
  • [23]. Zhou, B., Wang, X., Zhang, S., Li, Z., Sun, S., Shu, K., & Sun, Q., Comparing factors affecting injury severity of passenger car and truck drivers, IEEE Access, 8, 153849-153861, 2020.
  • [24]. Rezapour, M., Molan, A. M., & Ksaibati, K., Application of multinomial regression model to identify parameters impacting traffic barrier crash severity, The Open Transportation Journal, 13(1), 2019.
  • [25]. Aljarrah, M. F., Khasawneh, M. A., & Al-Omari, A. A., Investigating Key Factors Influencing the Severity of Drivers Injuries in Car Crashes Using Supervised Machine Learning Techniques, Journal of Engineering Science & Technology Review, 12(4), 2019.
  • [26]. Sarkar, S., Raj, R., Vinay, S., Maiti, J., & Pratihar, D. K., An optimization-based decision tree approach for predicting slip-trip-fall accidents at work, Safety science, 118, 57-69, 2019.
  • [27]. Basha, S. A., & Maiti, J., Relationships of demographic factors, job risk perception and work injury in a steel plant in India, Safety science, 51(1), 374-381, 2013.
  • [28]. Park, R. M., Ahn, Y. S., Stayner, L. T., Kang, S. K., & Jang, J. K., Mortality of iron and steel workers in Korea, American journal of industrial medicine, 48(3), 194-204, 2005.
  • [29]. Konstandinidou, M., Nivolianitou, Z., Kefalogianni, E., & Caroni, C., In-depth analysis of the causal factors of incidents reported in the Greek petrochemical industry, Reliability Engineering & System Safety, 96(11), 1448-1455, 2011.
  • [30]. Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X., Applied logistic regression, 398. John Wiley & Sons, 2013.
  • [31]. Kwak, C., & Clayton-Matthews, A., Multinomial logistic regression, Nursing research, 51(6), 404-410, 2002.
  • [32]. Güyagüler, T., & Bozkurt, R., Kömür Madenciliğinde Meydana Gelen İş Kazalarının. Maliyetleri, Türkiye, 8, 331-343, 1992.
  • [33]. Collie, A., Simpson, P. M., Cameron, P. A., Ameratunga, S., Ponsford, J., Lyons, R. A., ... & Gabbe, B. J., Patterns and predictors of return to work after major trauma: a prospective, population-based registry study, Annals of surgery, 269(5), 972-978, 2019.
  • [34]. Sze, N. N., & Song, Z., Factors contributing to injury severity in work zone related crashes in New Zealand, International journal of sustainable transportation, 13(2), 148-154, 2019.
  • [35]. Tait, R. C., & Chibnall, J. T., Workers’ compensation claimants with low back pain: the role of dissatisfaction in the transition to disability, Psychological injury and law, 9(1), 16-22, 2016.
  • [36]. Palei, S. K., Karmakar, N. C., & Reddy, R. S., Effects of demography and occupational traits on consequence of injury of underground coal miners, In 2014 IEEE International Conference on Industrial Engineering and Engineering Management, Selangor-Malaysia, 1260-1264, 2014.
  • [37]. Gholizadeh, P., & Esmaeili, B., Developing a multi-variate logistic regression model to analyze accident scenarios: Case of electrical contractors, International journal of environmental research and public health, 17(13), 4852, 2020.
  • [38]. Lee, W., Lee, J., Kim, U. J., Yoon, J. H., Choi, W. J., Ham, S., ... & Kang, S. K., Working conditions and mental health status related with occupational injury of Korean outdoor workers, Journal of occupational and environmental medicine, 62(7), e334-e339, 2020.
  • [39]. Fabiano, B., Currò, F., Reverberi, A. P., & Pastorino, R., A statistical study on temporary work and occupational accidents: specific risk factors and risk management strategies, Safety science, 46(3), 535-544, 2008.
  • [40]. Gholizadeh, P., Onuchukwu, I. S., & Esmaeili, B., Trends in Catastrophic Occupational Incidents among Electrical Contractors, 2007–2013, International Journal of Environmental Research and Public Health, 18(10), 5126, 2021.
  • [41]. Mosquera, R., Parra, L., Ledesma, A. J., & Bonilla, H. F., Predicción de la accidentalidad laboral en la industria de pulpa y papel usando algoritmos de clasificación, Información tecnológica, 32(1), 133-142, 2021.
  • [42]. Sarkar, S., Vinay, S., Raj, R., Maiti, J., & Mitra, P., Application of optimized machine learning techniques for prediction of occupational accidents, Computers & Operations Research, 106, 210-224, 2019.
  • [43]. Şahin, D. Ö., Şirin, B., Akleylek, S., & Kılıç, E., Work Accident Analysis with Machine Learning Techniques, In 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo- Bosnia Herzegovina, 215-219, 2018.
  • [44]. Shirali, G. A., Noroozi, M. V., & Malehi, A. S., Predicting the outcome of occupational accidents by CART and CHAID methods at a steel factory in Iran, Journal of public health research, 7(2), 74-80, 2018.
  • [45]. Sarkar, S., Pateshwari, V., & Maiti, J., Predictive model for incident occurrences in steel plant in India, In 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi-India, 1-5, 2017.
  • [46]. Comberti, L., Baldissone, G., & Demichela, M., Workplace accidents analysis with a coupled clustering methods: Som and k-means algorithms, Chemical Engineering Transactions, 43, 1261-1266, 2015.
  • [47]. Sanmiquel, L., Rossell, J. M., & Vintró, C., Study of Spanish mining accidents using data mining techniques, Safety science, 75, 49-55, 2015.
  • [48]. Cheng, C. W., Yao, H. Q., & Wu, T. C., Applying data mining techniques to analyze the causes of major occupational accidents in the petrochemical industry, Journal of Loss Prevention in the Process Industries, 26(6), 1269-1278, 2013.
  • [49]. Cheng, C. W., Leu, S. S., Cheng, Y. M., Wu, T. C., & Lin, C. C., Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan's construction industry, Accident Analysis & Prevention, 48, 214-222, 2012.
  • [50]. Reyes-Martínez, R. M., Maldonado-Macías, A., & Prado-León, L. R., Human factors identification and classification related to accidents’ causality on hand injuries in the manufacturing industry, Work, 3155-3163, 2012.
  • [51]. Ciarapica, F. E., & Giacchetta, G., Classification and prediction of occupational injury risk using soft computing techniques: An Italian study, Safety science, 47(1), 36-49, 2009.
  • [52]. Bevilacqua, M., Ciarapica, F. E., & Giacchetta, G., Industrial and occupational ergonomics in the petrochemical process industry: A regression trees approach, Accident Analysis & Prevention, 40(4), 1468-1479, 2008.
  • [53]. Liao, C. W., Perng, Y. H., & Chiang, T. L., Discovery of unapparent association rules based on extracted probability, Decision Support Systems, 47(4), 354-363, 2009.
  • [54]. McFadden, D., Conditional logit analysis of qualitative choice behavior. In Frontiers in Econometrics, edited by P. Zarembka, Academic Press, New York, 1974.
  • [55]. Şengül, Z., Ege Bölgesinde Arıcılık Yapan İşletmelerin Sürdürülebilirlik Yönünden Değerlendirilmesi, Doktora Tezi, Fen Bilimleri Enstitütsü, Ege Üniversitesi, İzmir, 2020.
  • [56]. El-Habil, A. M., An application on multinomial logistic regression model. Pakistan journal of statistics and operation research, 271-291, 2012.
  • [57]. Katz, M. H., Multivariable analysis: a practical guide for clinicians and public health researchers, Cambridge university press, 2011.
  • [58]. Liao, T. F. F., & Liao, T. F., Interpreting probability models: Logit, probit, and other generalized linear models, 101, Sage, 1994.
  • [59]. Goplerud, M., A Multinomial Framework for Ideal Point Estimation, Political Analysis, 27(1), 69-89, 2019.
  • [60]. Erkan, A. R. I., Using multinomial logistic regression to examine the relationship between children’s work status and demographic characteristics, Siyaset, Ekonomi ve Yönetim Araştırmaları Dergisi, 4(1), 77-93, 2016.
  • [61]. Chen, Z., & Fan, W. D., A multinomial logit model of pedestrian-vehicle crash severity in North Carolina, International journal of transportation science and technology, 8(1), 43-52, 2019.
  • [62]. Izadi, N., Aminian, O., & Esmaeili, B., Occupational Accidents in Iran: Risk Factors and Long Term Trend (2007–2016), Journal of research in health sciences, 19(2), e00448, 2019.
  • [63]. Al-Behadili, H. N. K., & Ku-Mahamud, K. R., Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification, Journal of Information and Communication Technology, 20(3), 391-422, 2021.
  • [64]. Revathy, G., Kumar, P. S., & Rajendran, V., Development of IDS using mining and machine learning techniques to estimate DoS malware, International Journal of Computational Science and Engineering, 24(3), 259-275, 2021.
  • [65]. Khalifa, R. M., Yacout, S., & Bassetto, S. 2021, Developing machine-learning regression model with Logical Analysis of Data (LAD), Computers & Industrial Engineering, 151, 106947, 2021.
  • [66]. Rokach, L., Romano, R., & Maimon, O., Mining manufacturing databases to discover the effect of operation sequence on the product quality, Journal of Intelligent manufacturing, 19(3), 313-325, 2008.
  • [67]. Stallard, T., & Levitt, K., Automated analysis for digital forensic science: Semantic integrity checking, In 19th Annual Computer Security Applications Conference, DC-United States, 160-167, 2003.
  • [68]. Hur, J., Lee, H., & Baek, J. G., An intelligent manufacturing process diagnosis system using hybrid data mining, In Industrial Conference on Data Mining, Berlin- Heidelberg, 561-575, 2006.
  • [69]. Sadic, S., & Kayakutlu, G., Integrating decision trees and cognitive maps for market segmentation in service sector, In PICMET'07-2007 Portland International Conference on Management of Engineering & Technology, Portland- OR USA 2748-2754, 2007.
  • [70]. Lin, J., A web forensic system based on semantic checking, In 2008 International Symposium on Computational Intelligence and Design, Wuhan, 1, 99-102, 2008.
  • [71]. Jun, L. I. U., Algorithm for decision tree construction for incompatible table, Journal of Hohai University (Natural Sciences), 2, 2013.
  • [72]. Gupta, P., Mehrotra, D., & Sharma, T. K., Role of decision tree in supplementing tacit knowledge for Hypothetico-Deduction in higher Education, International Journal of System Assurance Engineering and Management, 9(1), 82-90, 2018.
  • [73]. Qiong-sheng, Z., Ming-quan, W., Tong-xuan, L., & Xiao-wei, C., An Algorithm for Decision Tree Construction Based on Degree of Rough Classification, In 2010 International Conference on Artificial Intelligence and Computational Intelligence, Washington-DC United States 3, 23-29, 2010.
  • [74]. Xun, Y., Yin, Q., Zhang, J., Yang, H., & Cui, X., A novel discretization algorithm based on multi-scale and information entropy, Applied Intelligence, 51(2), 991-1009, 2021. [75]. Uncu, I. S., & Kayakus, M., Determınation of Effıcıent Light Source For Wheat Plant By Using Decision Trees Method, Fresenius Environmental Bulletin, 30(3), 2807-2812, 2021.
  • [76]. Horng, S. C., Yang, F. Y., & Lin, S. S., Hierarchical fuzzy clustering decision tree for classifying recipes of ion implanter, Expert Systems with Applications, 38(1), 933-940, 2011.
  • [77]. Pei, D., Gong, Y., Kang, H., Zhang, C., & Guo, Q., Accurate and rapid screening model for potential diabetes mellitus, BMC medical informatics and decision making, 19(1), 1-8, 2019.
  • [78]. Yang, T., Zhao, B., & Pei, D., Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach, Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 14, 3437, 2021.
  • [79]. Chawla, N. V., Bowyer, K. W., Hall, L. O., & KEgelmeyer, W. P., SMOTE: synthetic minority over-sampling technique, Journal of artificial intelligence research, 16, 321-357, 2002. [80]. Jiawei, H., & Kamber, M., Data mining: conceptsand techniques. M, SanFrancisco, Morgan Kaufman Publish—ers, 10-120, 2001.
  • [81]. Witten, D. M., Classification and clustering of sequencing data using a Poisson model, The Annals of Applied Statistics, 5(4), 2493-2518, 2011.
  • [82]. Maimon, O., & Rokach, L. (Eds.), Data mining and knowledge discovery handbook, 2005.
  • [83]. Buckland, M., & Gey, F., The relationship between recall and precision, Journal of the American society for information science, 45(1), 12-19, 1994.
  • [84]. Coşkun, C., & Baykal, A., Veri madenciliğinde sınıflandırma algoritmalarının bir örnek üzerinde karşılaştırılması, Akademik Bilişim, 2011, 1-8, 2011.
  • [85]. Mutlu, N. G., & Altuntas, S., Assessment of occupational risks In Turkish manufacturing systems with data-driven models, Journal of Manufacturing Systems, 53, 169-182, 2019.
  • [86]. Köse, N., & Ersöz, F., Veri Madenciliğinde Karar Ağacı Algoritmaları İle Demir Çelik Endüstrisinde İş Kazaları Üzerine Bir Uygulama, Avrupa Bilim ve Teknoloji Dergisi, 397-407, 2020.
  • [87]. Hajakbari, M. S., & Minaei-Bidgoli, B., A new scoring system for assessing the risk of occupational accidents: A case study using data mining techniques with Iran's Ministry of Labor data, Journal of Loss Prevention in the Process Industries, 32, 443-453, 2014.
  • [88]. Declaration on Occupational Health and Safety in Workplace Hazard Classes, No: 28509. https://www.resmigazete.gov.tr/eskiler/2012/12/20121226-11.htm, Yayın tarihi 2012. Erişim Tarihi Ekim 28, 2021.
  • [89]. Hausman, J. ve McFadden, D., Spesification Tests for the Multinomial Logit Model, Econometrica, 52, 1219-1240, 1984.
  • [90]. Farahbod, H., Ghiyasi, S., & Soltanzadeh, A., Association of Non-Organizational Factors and Occupational Accidents: A Field Study based on Structural Equation Modeling. Journal of Occupational Health and Epidemiology, 10(1), 31-38, 2021.
  • [91]. Kurt Gök, D., Ünal, İ., & Aslan-Kara, K., Evaluation of the effects of shift work on parasomnia prevalence, Chronobiology International, 38(10), 1500-1506, 2021.
  • [92]. Rajaratnam, S. M., Howard, M. E., & Grunstein, R. R., Sleep loss and circadian disruption in shift work: health burden and management, Medical Journal of Australia, 199, S11-S15, 2013.
  • [93]. Szóstak, M., Analysis of occupational accidents in the construction industry with regards to selected time parameters, Open Engineering, 9(1), 312-320, 2019.
  • [94]. Pietilä, J., Räsänen, T., Reiman, A., Ratilainen, H., & Helander, E., Characteristics and determinants of recurrent occupational accidents, Safety science, 108, 269-277, 2018.
  • [95]. Allman, M., Allmanová, Z., & Jankovský, M., Is cable yarding a dangerous occupation? A Survey from the public and private sector, Central European Forestry Journal, 64(2), 127-132, 2018.
  • [96]. de Medeiros Prudêncio, A. L., Marques, B. G., Aguiar, D. R., Lima, L. C., Cabral, L. D., Quadros, R. W., & Magajewski, F. R., Socioeconomic and demographic profile of occupational morbidity and mortality in Brazil from 2009 to 2016, Revista brasileira de medicina do trabalho, 19(1), 68, 2021.
  • [97]. Rembiasz, M., Impact of employee age on the safe performance of production tasks. In MATEC Web of Conferences, 94, 07009, 2017.
  • [98]. Yi, K. H., & Lee, S. S., A policy intervention study to identify high-risk groups to prevent industrial accidents in Republic of Korea, Safety and health at work, 7(3), 213-217, 2016.
  • [99]. Nola, A., Cattaneo, G., Maiocchi, A., Gariboldi, C., Rocchi, R., Cavallaro, S., ... & Bassino, P., Occupational accidents in temporary work, La Medicina Del Lavoro, 92(4), 281-285, 2001.
  • [100]. Nodoushan, R. J., Akhavan, A., Miyanshahri, M. E., & Anoosheh, V. S., Investigation of the relationship between occupational cognitive failures and work-related accidents in heavy equipment operators of Shahid Rajaee port complex, Journal of Education and Health Promotion, 9(1), 189, 2020.
  • [101]. Abdar, M., A survey and compare the performance of IBM SPSS modeler and rapid miner software for predicting liver disease by using various data mining algorithms, Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, 36(3), 3230-3241, 2015.
There are 98 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Nazlı Gülüm Mutlu 0000-0003-0210-5175

Sibel Selim 0000-0002-8464-588X

Serkan Altuntaş 0000-0003-4383-4710

Early Pub Date November 24, 2023
Publication Date November 30, 2023
Submission Date June 17, 2022
Acceptance Date June 4, 2023
Published in Issue Year 2024 Volume: 39 Issue: 2

Cite

APA Mutlu, N. G., Selim, S., & Altuntaş, S. (2023). Türk imalat sistemlerinde iş kazalarındaki örüntülerin çok durumlu logit model’e dayalı bir yaklaşımla belirlenmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39(2), 1049-1066. https://doi.org/10.17341/gazimmfd.1131524
AMA Mutlu NG, Selim S, Altuntaş S. Türk imalat sistemlerinde iş kazalarındaki örüntülerin çok durumlu logit model’e dayalı bir yaklaşımla belirlenmesi. GUMMFD. November 2023;39(2):1049-1066. doi:10.17341/gazimmfd.1131524
Chicago Mutlu, Nazlı Gülüm, Sibel Selim, and Serkan Altuntaş. “Türk Imalat Sistemlerinde Iş kazalarındaki örüntülerin çok Durumlu Logit model’e Dayalı Bir yaklaşımla Belirlenmesi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39, no. 2 (November 2023): 1049-66. https://doi.org/10.17341/gazimmfd.1131524.
EndNote Mutlu NG, Selim S, Altuntaş S (November 1, 2023) Türk imalat sistemlerinde iş kazalarındaki örüntülerin çok durumlu logit model’e dayalı bir yaklaşımla belirlenmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39 2 1049–1066.
IEEE N. G. Mutlu, S. Selim, and S. Altuntaş, “Türk imalat sistemlerinde iş kazalarındaki örüntülerin çok durumlu logit model’e dayalı bir yaklaşımla belirlenmesi”, GUMMFD, vol. 39, no. 2, pp. 1049–1066, 2023, doi: 10.17341/gazimmfd.1131524.
ISNAD Mutlu, Nazlı Gülüm et al. “Türk Imalat Sistemlerinde Iş kazalarındaki örüntülerin çok Durumlu Logit model’e Dayalı Bir yaklaşımla Belirlenmesi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39/2 (November 2023), 1049-1066. https://doi.org/10.17341/gazimmfd.1131524.
JAMA Mutlu NG, Selim S, Altuntaş S. Türk imalat sistemlerinde iş kazalarındaki örüntülerin çok durumlu logit model’e dayalı bir yaklaşımla belirlenmesi. GUMMFD. 2023;39:1049–1066.
MLA Mutlu, Nazlı Gülüm et al. “Türk Imalat Sistemlerinde Iş kazalarındaki örüntülerin çok Durumlu Logit model’e Dayalı Bir yaklaşımla Belirlenmesi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 39, no. 2, 2023, pp. 1049-66, doi:10.17341/gazimmfd.1131524.
Vancouver Mutlu NG, Selim S, Altuntaş S. Türk imalat sistemlerinde iş kazalarındaki örüntülerin çok durumlu logit model’e dayalı bir yaklaşımla belirlenmesi. GUMMFD. 2023;39(2):1049-66.