DIAGNOSIS OF DIABETES MELLITUS USING STATISTICAL METHODS AND MACHINE LEARNING ALGORITHMS

Yıl 2018, Cilt: 36 Sayı: 4, 1265 - 1282, 01.12.2018

Ebru Pekel Tuncay Özcan

Öz

The early diagnosis of the diabetes condition is crucial for cure process, because an early diagnosis provides the ease of treatment for the patient and the physician. At this point, statistical methods and data mining algorithms can provide important opportunities for early diagnosis of diabetes mellitus. In the literature, many studies have been published for solution of this problem. In this study, firstly, these studies are analyzed in detail and classified according to their methodologies and solution approaches. The main aim of this paper is to provide the comprehensive and detailed review of the diagnosis of diabetes by statistical methods and machine learning algorithms. Also, this paper presents a literature review on the diagnosis diabetes up to the end of 2017. It's identified over 425 papers, highly cited 100 ones are presented in detailed. This paper provides to guide future research and knowledge accumulation and creation of classification and prediction techniques in diagnosis of diabetes. This study shows it is clear that the combination of different machine learning algorithms and optimization models can lead to more meaningful and powerful results.

Anahtar Kelimeler

Classification, diabetes mellitus, machine learning, prediction, statistical methods.

Kaynakça

• [1] Gavin, J. R. (1998). New classification and diagnostic criteria for diabetes mellitus. Clinical cornerstone, 1(3), 1-12.
• [2] Gray, L. J., & Khunti, K., (2013). Type 2 diabetes risk prediction—Do biomarkers increase detection?. Diabetes research and clinical practice, 101(3), 245-247.
• [3] Kayaer, K., & Yıldırım, T., (2003), Medical diagnosis on Pima Indian diabetes using general regression neural networks. In Proceedings of the international conference on artificial neural networks and neural information processing (ICANN/ICONIP) (pp. 181-184).
• [4] Magliano, D. J., Peeters, A., Vos, T., Sicree, R., Shaw, J., Sindall, C., Zimmet, P. Z., (2009), Projecting the burden of diabetes in Australia–what is the size of the matter?. Australian and New Zealand journal of public health, 33(6), 540-543.
• [5] Boyle, J. P., Honeycutt, A. A., Narayan, K. V., Hoerger, T. J., Geiss, L. S., Chen, H., & Thompson, T. J. (2001). Projection of diabetes burden through 2050. Diabetes care, 24(11), 1936-1940.
• [6] McEwan, P., Peters, J. R., Bergenheim, K., & Currie, C. J., (2006), Evaluation of the costs and outcomes from changes in risk factors in type 2 diabetes using the Cardiff stochastic simulation cost-utility model (DiabForecaster). Current medical research and opinion, 22(1), 121-129.
• [7] Shi, L., van Meijgaard, J., & Fielding, J., (2011), Forecasting diabetes prevalence in California: a microsimulation. Prev Chronic Dis, 8(4), A80.
• [8] Upadhyay A., Patel V. R., (2016), Comparative Study - Prediction of Diabetes and Heart Disease using Data Mining Approaches, International Journal of Engineering Technology, Management and Applied Sciences, 4(1), 70-76
• [9] Ruwaard, D., Hoogenveen, R. T., Verkleij, H., Kromhout, D., Casparie, A. F., & Van der Veen, E. A. (1993). Forecasting the number of diabetic patients in The Netherlands in 2005. American journal of public health, 83(7), 989-995.
• [10] Gu, D., Reynolds, K., Duan, X., Xin, X., Chen, J., Wu, X., (2003), Prevalence of diabetes and impaired fasting glucose in the Chinese adult population: International Collaborative Study of Cardiovascular Disease in Asia (InterASIA). Diabetologia, 46(9), 1190-1198.
• [11] Rosenthal A. D. Et al., (2004), Body fat distribution and risk of diabetes among Chinese women, International Journal of Obesity, 28, 594-599.
• [12] McNeely, M. J., & Boyko, E. J., (2004), Type 2 diabetes prevalence in Asian Americans. Diabetes Care, 27(1), 66-69.
• [13] Cox, J., Coupland, C., Robson, J., Sheikh, A., & Brindle, P. (2009). Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. Bmj, 338, b880.
• [14] Holman, N., Forouhi, N. G., Goyder, E., & Wild, S. H., (2011), The Association of Public Health Observatories (APHO) diabetes prevalence model: estimates of total diabetes prevalence for England, 2010–2030. Diabetic Medicine, 28(5), 575-582
• [15] Chao, C., Song, Y., Cook, N., Tseng, C. H., Manson, J. E., Eaton, C., Liu, S. (2010). The lack of utility of circulating biomarkers of inflammation and endothelial dysfunction for type 2 diabetes risk prediction among postmenopausal women: the Women's Health Initiative Observational Study. Archives of internal medicine, 170(17), 1557-1565.
• [16] Ahasan N., Islam, Z., Alam, B., Miah, T., Nur, Z.,(2011), Prevalence and Risk Factors of Type 2 Diabetes Mellitus Among Secretariat Employees of Bangladesh, Bangladesh Journals Online, 12(2) : 125-130
• [17] Onat, A., Can, G., Yüksel, H., Ayhan, E., Dogan, Y., & Hergenç, G., (2011), An algorithm to predict risk of type 2 diabetes in Turkish adults: contribution of C-reactive protein. Journal of endocrinological investigation, 34(8), 580-586.
• [18] Lau, R. S., Ohinmaa, A., & Johnson, J. A., (2011), Predicting the Future Burden of Diabetes in Alberta from 2008 to 2035. Canadian Journal of Diabetes, 35(3), 274-281.
• [19] Lee, J. W. R., Brancati, F. L., & Yeh, H. C., (2011), Trends in the prevalence of type 2 diabetes in Asians versus Whites. Diabetes care, 34(2), 353-357.
• [20] Abbasi, A., Bakker, S. J., Corpeleijn, E., Gansevoort, R. T., Gans, R. O., Peelen, L. M. & Beulens, J. W. (2012). Liver function tests and risk prediction of incident type 2 diabetes: evaluation in two independent cohorts. PloS one, 7(12), e51496.
• [21] Kwak, S. H., Choi, S. H., Kim, K., Jung, H. S., Cho, Y. M., Lim, S., Jang, H. C., (2013), Prediction of type 2 diabetes in women with a history of gestational diabetes using a genetic risk score. Diabetologia, 56(12), 2556-2563.
• [22] Senthilkumar D., Paulraj S., (2013), Diabetes Disease Diagnosis Using Multivariate Adaptive Regression Splines, International Journal of Engineering and Technology, 5(5), 3922-3929.
• [23] Mühlenbruch, K., Joost, H. G., Boeing, H., & Schulze, M. B., (2014), Risk prediction for type 2 diabetes in the German population with the updated German Diabetes Risk Score (GDRS). Ernährungs Umsch, 61, 90-3.
• [24] Nanri A. et al.,, (2015), Development of Risk Score for Predicting 3-Year Incidence of Type 2 Diabetes: Japan Epidemiology Collaboration on Occupational Health Study, PLOS ONE 10(11):e0142779.
• [25] Razavian, N., Blecker, S., Schmidt, A. M., Smith-McLallen, A., Nigam, S., & Sontag, D., (2015), Population-level prediction of type 2 diabetes from claims data and analysis of risk factors. Big Data, 3(4), 277-287.
• [26] Hu, H., Huff, C. D., Yamamura, Y., Wu, X., & Strom, S. S., (2015), The relationship between Native American ancestry, body mass index and diabetes risk among Mexican-Americans. PloS one, 10(10).
• [27] Alby S. and Shivakumar B. L., (2016), A Prediction Model for Type 2 Diabetes Risk Among Indian Women, ARPN Journal of Engineering and Applied Sciences, 11 (3), 2037-2043.
• [28] Polat, K., Şahan, S., & Güneş, S., (2006), A new method to medical diagnosis: Artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia. Expert Systems with Applications, 31(2), 264-269.
• [29] Temurtas, H., Yumusak, N., & Temurtas, F., (2009), A comparative study on diabetes disease diagnosis using neural networks. Expert Systems with applications, 36(4), 8610-8615.
• [30] Polat, K., & Güneş, S., (2007), An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digital Signal Processing, 17(4), 702-710.
• [31] Dogantekin, E., Dogantekin, A., Avci, D., & Avci, L. (2010). An intelligent diagnosis system for diabetes on linear discriminant analysis and adaptive network based fuzzy inference system: LDA-ANFIS. Digital Signal Processing, 20(4), 1248-1255.
• [32] Kala, R., Shukla, A., & Tiwari, R., (2009), Comparative analysis of intelligent hybrid systems for detection of PIMA indian diabetes. In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on (pp. 947-952). IEEE.
• [33] Drezet, P. M., & Harrison, R. F. (2001). A new method for sparsity control in support vector classification and regression. Pattern Recognition, 34(1), 111
• [34] Georga, E. I., Protopappas, V. C., Ardigò, D., Marina, M., Zavaroni, I., Polyzos, D., & Fotiadis, D. I., (2013). Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression. IEEE journal of biomedical and health informatics, 17(1), 71-81.
• [35] Karahoca, A., Karahoca, D., & Kara, A., (2009), Diagnosis of diabetes by using adaptive neuro fuzzy inference systems. In Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009. ICSCCW 2009. Fifth International Conference on (pp. 1-4). IEEE.
• [36] Sharifi, A., Vosolipour, A., Sh, M. A., & Teshnehlab, M., (2008), Hierarchical Takagi-Sugeno type fuzzy system for diabetes mellitus forecasting. In Machine Learning and Cybernetics, 2008 International Conference on (Vol. 3, pp. 1265-1270). IEEE.
• [37] Smith, J. W., Everhart, J. E., Dickson, W. C., Knowler, W. C., & Johannes, R. S., (1988), Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Annual Symposium on Computer Application in Medical Care (p. 261). American Medical Informatics Association.
• [38] Farhanah, S., Jafan, B., & Ali, D. M. (2005). Diabetes Mellitus Forecast using Artificial Neural Networks (ANN). In Asian Conference on sensors and the international conference on new techniques in pharamaceutical and medical research proceedings (IEEE) (pp. 135-138).
• [39] Huang, Y., McCullagh, P., Black, N., & Harper, R., (2007), Feature selection and classification model construction on type 2 diabetic patients’ data. Artificial intelligence in medicine, 41(3), 251.
• [40] Barakat, N., Bradley, A. P., & Barakat, M. N. H. (2010). Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE transactions on information technology in biomedicine, 14(4), 1114-1120.
• [41] Acar, E., Özerdem, M. S., & Akpolat, V. (2011). Diabetes Mellitus Forcast Using Various Types of Artificial Neural Networks. In 6th International Advanced Technologies Symposium (pp. 196-201).
• [42] Karegowda, A. G., Manjunath, A. S., & Jayaram, M. A., (2011), Application of genetic algorithm optimized neural network connection weights for medical diagnosis of pima Indians diabetes. International Journal on Soft Computing, 2(2), 15-23.
• [43] Luangruangrong, W., Rodtook, A., & Chimmanee, S., (2012), Study of Type 2 diabetes risk factors using neural network for Thai people and tuning neural network parameters. In Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on (pp. 991-996). IEEE.
• [44] Mani, S., Chen, Y., Elasy, T., Clayton, W., & Denny, J. (2012). Type 2 diabetes risk forecasting from EMR data using machine learning. In AMIA annual symposium proceedings (Vol. 2012, p. 606). American Medical Informatics Association.
• [45] Mohamed, N., Ahmad, W. M. A. W., & Aleng, N. A., (2013), Modeling Multi Layer Feed-forward Neural Network Model on the Influence of Hypertension and Diabetes Mellitus on Family History of Heart Attack in Male Patients. Applied Mathematical Sciences, 7(41), 2047-2053.
• [46] Mühlenbruch, K., Jeppesen, C., Joost, H. G., Boeing, H., & Schulze, M. B., (2013), The value of genetic information for diabetes risk prediction–differences according to sex, age, family history and obesity. PLoS One, 8(5), e64307.
• [47] Khan, N., Gaurav, D., & Kandl, T., (2013), Performance evaluation of Levenberg-Marquardt technique in error reduction for diabetes condition classification. Procedia Computer Science, 18, 2629-2637.
• [48] Kumari, V. A., & Chitra, R., (2013), Classification of diabetes disease using support vector machine. International Journal of Engineering Research and Applications, 3(2), 1797-1801.
• [49] Ahmed, K. A. W. S. A. R., Jesmin, T. A. S. N. U. B. A., Fatima, U. S. H. I. N., Moniruzzaman, M., Emran, A. A., & Rahman, M. Z. (2012). Intelligent and effective diabetes risk prediction system using data mining. Orient J Comput Sci Technol, 5, 215-221.
• [50] Aslam, M. W., Zhu, Z., & Nandi, A. K. (2013). Feature generation using genetic programming with comparative partner selection for diabetes classification. Expert Systems with Applications, 40(13), 5402-5412
• [51] Saxena, K., Khan, Z., & Singh, S., (2014), Diagnosis of Diabetes Mellitus using K Nearest Neighbor Algorithm. International Journal of Computer Science Trends and Technology (IJCST).
• [52] Jhaldiyal T. and Mishra P. K., (2014), Analysis and Prediction of Diabetes Mellitus Using PCA, REP and SVM, International Journal of Engineering and Technical Research (IJETR), 2 (8), 164-166.
• [53] Sarwar A. and Sharma V., (2014), Comparative analysis of machine learning techniques in prognosis of type II diabetes, Al & Society, 29(1), 123-129.
• [54] Thangaraju, P., Deepa, B., & Karthikeyan, T., (2014), Comparison of Data mining Techniques for Forecasting Diabetes Mellitus. International Journal of Advanced Research in Computer and Communication Engineering, 3(8).
• [55] Diwani, S. A., & Sam, A. (2014). Diabetes Forecasting Using Supervised Learning Techniques. Advances in Computer Science: an International Journal, 3(5), 10-18.
• [56] Shigemizu, D., Abe, T., Morizono, T., Johnson, T. A., Boroevich, K. A., Hirakawa, Y., & Maeda, S., (2014), The construction of risk prediction models using GWAS data and its application to a type 2 diabetes prospective cohort. PLoS One, 9(3), e92549.
• [57] Chen, H., Tan, C., Lin, Z., & Wu, T. (2014). The diagnostics of diabetes mellitus based on ensemble modeling and hair/urine element level analysis. Computers in biology and medicine, 50, 70-75.
• [58] Utomo, C. P., Kardiana, A., & Yuliwulandari, R., (2014), Breast cancer diagnosis using artificial neural networks with extreme learning techniques. International Journal of Advanced Research in Artificial Intelligence, 3(7), 10-14.
• [59] Tanamas, S. K., Magliano, D. J., Balkau, B., Tuomilehto, J., Kowlessur, S., Söderberg, S., Shaw, J. E., (2015), The performance of diabetes risk prediction models in new populations: the role of ethnicity of the development cohort. Acta diabetologica, 52(1), 91-101.
• [60] Chen, L. S., & Cai, S. J. (2015). Neural-Network-Based Resampling Method for Detecting Diabetes Mellitus. Journal of Medical and Biological Engineering, 35(6), 824-832.
• [61] Heydari, M., Teimouri, M., Heshmati, Z., & Alavinia, S. M., (2016), Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran. International Journal of Diabetes in Developing Countries, 36(2), 167-173.
• [62] Kose, U., Guraksin, G. E., & Deperlioglu, O., (2016), Cognitive development optimization algorithm based support vector machines for determining diabetes. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 7(1), 80-90.
• [63] Amanda A. H., Boyle, JP., Broglio KR, Thompson TJ, Hoerger, TJ., Geiss, LS., Narayan, HM., 2003, A Dynamic Markov Model for Forecasting Diabetes Prevalence in the United States through 2050, Health Care Management Science, 6(3), 155-164.
• [64] Boyle, J. P., Thompson, T. J., Gregg, E. W., Barker, L. E., & Williamson, D. F. (2010). Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Population health metrics, 8(1), 29.
• [65] Honeycutt, A. A., Boyle, J. P., Broglio, K. R., Thompson, T. J., Hoerger, T. J., Geiss, L. S., & Narayan, K. V., (2003), A dynamic Markov model for forecasting diabetes prevalence in the United States through 2050. Health care management science, 6(3), 155-164.
• [66] Narayan, K. V., Boyle, J. P., Geiss, L. S., Saaddine, J. B., & Thompson, T. J., (2006), Impact of recent increase in incidence on future diabetes burden. Diabetes care, 29(9), 2114-2116.
• [67] Huang, E. S., Basu, A., O'grady, M., & Capretta, J. C., (2009), Projecting the future diabetes population size and related costs for the US. Diabetes care, 32(12), 2225-2229.
• [68] O'Flaherty M., Critchley J., Wild S., (2010), "018 Forecating Diabetes Prevalence Using a Simple Model: England and Wales 1993-2006", Journal of Eğidemiology & Community Health 2010, 64:A7
• [69] O'flaherty, M., Critchley, J., Wild, S., Unwin, N., & Capewell, S., (2011), O1-5.6 Forecasting Diabetes Prevalence: validation of a simple model with few data requirements. Journal of Epidemiology and Community Health, 65(Suppl 1), A17-A17.
• [70] Abu-Rmeileh, N. M., Husseini, A., O'Flaherty, M., Shoaibi, A., & Capewell, S. (2012). Forecasting prevalence of type 2 diabetes mellitus in Palestinians to 2030: validation of a predictive model. The Lancet, 380, S21.
• [71] Al Ali, R., Mzayek, F., Rastam, S., Fouad, F. M., O’Flaherty, M., Capewell, S., & Maziak, W. (2013). Forecasting future prevalence of type 2 diabetes mellitus in Syria. BMC Public Health, 13(1), 507.
• [72] Waldeyer, R., Brinks, R., Rathmann, W., Giani, G., & Icks, A., (2013), Projection of the burden of type 2 diabetes mellitus in Germany: a demographic modelling approach to estimate the direct medical excess costs from 2010 to 2040. Diabetic Medicine, 30(8), 999-1008.
• [73] Phan, T. P., Alkema, L., Tai, E. S., Tan, K. H., Yang, Q., Lim, W. Y., Chia, K. S., (2014), Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore. BMJ open diabetes research & care, 2(1), e000012.
• [74] Mutlu, F., Bener, A., Eliyan, A., Delghan, H., Nofal, E., Shalabi, L., & Wadi, N., (2014), Projection of diabetes burden through 2025 and contributing risk factors of changing disease prevalence: an emerging public health problem. J Diabetes Metab, 5(2), 1000341.
• [75] Saidi, O., O’Flaherty, M., Mansour, N. B., Aissi, W., Lassoued, O., Capewell, S., Romdhane, H. B., (2015), Forecasting Tunisian type 2 diabetes prevalence to 2027: validation of a simple model. BMC public health, 15(1), 104.
• [76] Utomo, C. P., Kardiana, A., Yuliwulandari, R. (2014). Breast cancer diagnosis using artificial neural networks with extreme learning techniques. International Journal of Advanced Research in Artificial Intelligence, 3(7), 10-14.
• [77] Davis, L. (1990). Hybrid genetic algorithms for machine learning. In Machine Learning, IEE Colloquium on (pp. 9-1). IET.
• [78] Kelly Jr, J. D., Davis, L. (1991). A Hybrid Genetic Algorithm for Classification. In IJCAI (Vol. 91, pp. 645-650).
• [79] Carvalho, D. R., & Freitas, A. A. (2004). A hybrid decision tree/genetic algorithm method for data mining. Information Sciences, 163(1), 13-35.
• [80] Lee, K. C., Han, I., & Kwon, Y. (1996). Hybrid neural network models for bankruptcy predictions. Decision Support Systems, 18(1), 63-72.
• [81] Qi, Y., Doermann, D., & DeMenthon, D. (2001). Hybrid independent component analysis and support vector machine learning scheme for face detection. In Acoustics, Speech, and Signal Processing, 2001. Proceedings.(ICASSP'01). 2001 IEEE International Conference on (Vol. 3, pp. 1481-1484). IEEE.
• [82] Pan, Z. S., Chen, S. C., Hu, G. B., & Zhang, D. Q. (2003, November). Hybrid neural network and C4. 5 for misuse detection. In Machine Learning and Cybernetics, 2003 International Conference on (Vol. 4, pp. 2463-2467). IEEE.
• [83] Niknam, T., & Amiri, B. (2010). An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Applied Soft Computing, 10(1), 183-197.
• [84] Li, L., Jiang, W., Li, X., Moser, K. L., Guo, Z., Du, L., ... & Rao, S. (2005). A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset. Genomics, 85(1), 16-23.
• [85] Min, S. H., Lee, J., & Han, I. (2006). Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert systems with applications, 31(3), 652-660.
• [86] Huerta, E. B., Duval, B., & Hao, J. K. (2006, April). A hybrid GA/SVM approach for gene selection and classification of microarray data. In Workshops on Applications of Evolutionary Computation (pp. 34-44). Springer, Berlin, Heidelberg.
• [87] Bies, R. R., Muldoon, M. F., Pollock, B. G., Manuck, S., Smith, G., & Sale, M. E. (2006). A genetic algorithm-based, hybrid machine learning approach to model selection. Journal of Pharmacokinetics and Pharmacodynamics, 33(2), 195-221.
• [88] Shon, T., & Moon, J. (2007). A hybrid machine learning approach to network anomaly detection. Information Sciences, 177(18), 3799-3821.
• [89] Kelly Jr, J. D., & Davis, L. (1991, August). A Hybrid Genetic Algorithm for Classification. In IJCAI (Vol. 91, pp. 645-650).
• [90] Şahan, S., Polat, K., Kodaz, H., & Güneş, S. (2007). A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Computers in Biology and Medicine, 37(3), 415-423.
• [91] Kim, H. J., & Shin, K. S. (2007). A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets. Applied Soft Computing, 7(2), 569-576.
• [92] Sivagaminathan, R. K., & Ramakrishnan, S. (2007). A hybrid approach for feature subset selection using neural networks and ant colony optimization. Expert systems with applications, 33(1), 49-60.
• [93] Huang, C. L., & Dun, J. F. (2008). A distributed PSO–SVM hybrid system with feature selection and parameter optimization. Applied soft computing, 8(4), 1381-1391.
• [94] Choudhry, R., & Garg, K. (2008). A hybrid machine learning system for stock market forecasting. World Academy of Science, Engineering and Technology, 39(3), 315-318.
• [95] Aci, M., İnan, C., & Avci, M. (2010). A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm. Expert Systems with Applications, 37(7), 5061-5067.
• [96] Tsai, C. F., & Wang, S. P. (2009, March). Stock price forecasting by hybrid machine learning techniques. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 1, No. 755, p. 60).
• [97] Kharrat, A., Gasmi, K., Messaoud, M. B., Benamrane, N., & Abid, M. (2010). A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine. Leonardo Journal of Sciences, 17(1), 71-82.
• [98] Yang, H., Liu, J., Sui, J., Pearlson, G., & Calhoun, V. D. (2010). A hybrid machine learning method for fusing fMRI and genetic data: combining both improves classification of schizophrenia. Frontiers in human neuroscience, 4.
• [99] Tahir, M. A., Bouridane, A., & Kurugollu, F. (2007). Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier. Pattern Recognition Letters, 28(4), 438-446.
• [100] Yang, F., Sun, T., & Zhang, C. (2009). An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization. Expert Systems with Applications, 36(6), 9847-9852.
• [101] Sartakhti, J. S., Zangooei, M. H., & Mozafari, K. (2012). Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA). Computer methods and programs in biomedicine, 108(2), 570-579.
• [102] Basari, A. S. H., Hussin, B., Ananta, I. G. P., & Zeniarja, J. (2013). Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization. Procedia Engineering, 53, 453-462.
• [103] Aziz, A. S. A., Hassanien, A. E., Hanaf, S. E. O., & Tolba, M. F. (2013, December). Multi-layer hybrid machine learning techniques for anomalies detection and classification approach. In Hybrid Intelligent Systems (HIS), 2013 13th International Conference on (pp. 215-220). IEEE.
• [104] Zheng, B., Yoon, S. W., & Lam, S. S. (2014). Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications, 41(4), 1476-1482.
• [105] Wong, L. Y., Toh, M. P. H. S., & Tham, L. W. C., (2017), Projection of prediabetes and diabetes population size in Singapore using a dynamic Markov model. Journal of diabetes, 9(1), 65-75.
• [106] Ahmed, K. A. W. S. A. R., Jesmin, T. A. S. N. U. B. A., Fatima, U. S. H. I. N., Moniruzzaman, M., Emran, A. A., & Rahman, M. Z. (2012). Intelligent and effective diabetes risk prediction system using data mining. Orient J Comput Sci Technol, 5, 215-221.
• [107] Jaafar, S. F. B., & Ali, D. M., (2005), Diabetes mellitus forecast using artificial neural network (ANN). In Sensors and the International Conference on new Techniques in Pharmaceutical and Biomedical Research, 2005 Asian Conference on (pp. 135-139). IEEE.
• [108] Luo G., (2016), Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction, Health Information Sciences and Systems.
• [109] Shi, L., van Meijgaard, J., & Fielding, J., (2011), Forecasting diabetes prevalence in California: a microsimulation. Prev Chronic Dis, 8(4), A80.
• [110] Sanidad, J. G., Bandala, A., Sanidad, B. G., & Marfil, S. S. Prediction of Diabetes on Women using Decision Tree Algorithm.
• [111] Parthiban, G., Rajesh, A., & Srivatsa, S. K. (2011). Diagnosis of heart disease for diabetic patients using naive bayes method. International Journal of Computer Applications, 24(3), 7-11.
• [112] Polat, K., Güneş, S., & Arslan, A. (2008). A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine. Expert systems with applications, 34(1), 482-487.
• [113] Yu, W., Liu, T., Valdez, R., Gwinn, M., & Khoury, M. J. (2010). Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC medical informatics and decision making, 10(1), 16.
• [114] Suhaimi, N., & Ismail, A. Comparing the Performance of Logistic Regression and Artificial Neural Networks Models: An Application to Type 2 Diabetes Mellitus.
• [115] Batista, G. E., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter, 6(1), 20-29.
• [116] Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.