Prediction of Lung Cancer with Fuzzy Logic Methods: A Systematic Review

Year 2024, Volume: 4 Issue: 2, 155 - 192, 01.10.2024

Beyza Aslan , Ouranıa Areta Hızıroğlu

Abstract

According to the World Health Organization (WHO), lung cancer is the primary cause of cancer-related deaths worldwide and is known to have the highest mortality rate among both men and women. Early and accurate detection of lung cancer can lead to better treatments and outcomes. Different methods can be used to diagnose a complex and uncertain disease, such as lung cancer, and fuzzy logic is one of these methods. The challenge of diagnosing lung cancer nodules, coupled with the high mortality rate of lung cancer, underscores the significance of using fuzzy logic. Fuzzy logic offers a problem-solving approach that relies on logical rules and if-then statements, incorporating human experience. There are many studies in the literature on the diagnosis of lung cancer with fuzzy logic approaches, and it is important to examine these studies to provide a general framework on this subject. Therefore, this systematic review aims to synthesize and evaluate the current evidence on the application of fuzzy logic methods in lung cancer prediction and diagnosis, and thus can provide a guide to researchers and decision makers who want to work in this field. The study followed the PRISMA guidelines for systematic reviews, ensuring a structured and transparent approach to the research process. Scopus, Web of Science (WoS), PubMed, and IEEE Explore databases were searched to find relevant studies, and appropriate studies were carefully reviewed. The inclusion and exclusion criteria were clearly defined, and the analysis process was performed independently. Out of 222 initially identified studies, 51 met the inclusion criteria and were analyzed in depth. The most commonly used fuzzy logic techniques were Fuzzy Rule-Based Systems, Fuzzy C-Means Clustering, and Fuzzy Inference Systems. Studies reported accuracy rates ranging from 85% to 98% in lung cancer prediction and diagnosis. Hybrid models combining fuzzy logic with other machine learning techniques showed particularly promising results. Fuzzy logic methods demonstrate significant potential in improving the accuracy of lung cancer prediction and diagnosis. However, further research is needed to standardize approaches and validate these methods in large-scale clinical settings. The integration of fuzzy logic with other artificial intelligence techniques presents a promising direction for future developments in lung cancer diagnostics.

Keywords

Fuzzy logic, lung cancer, prediction, diagnosis, systematic review

References

• [1] World Health Organization (2022). Cancer. https://www.who.int/news-room/fact-sheets/detail/cancer.
• [2] Aberle, D. R., Adams, A. M., Berg, C. D., Black, W. C., Clapp, J. D., Fagerstrom, R. M., ... & National Lung Screening Trial Research Team. (2011). Reduced lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine, 365(5), 395-409.
• [3] Chen, W., Wang, Y., Tian, D., & Yao, Y. (2023). CT lung nodule segmentation: A comparative study of-data preprocessing and deep learning models. IEEE Access, 11, 34925-34931.
• [4] Cancer today. (2022). International Agency for Research on Cancer. Retrieved September 26, 2024, from https://gco.iarc.who.int/today/en/dataviz/bars?mode=cancer&key=total&group_populations= 1&types=0&sort_by=value1&populations=900&multiple_populations=0&values_position=out&cancers_h=39
• [5] Cheng, J., Chen, W., Cao, Y., Xu, Z., Tan, Z., Zhang, X., … & Feng, J. (2020). Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nature Communications, 11(1), https://doi.org/10.1038/s41467-020-18685-1
• [6] Zadeh, L. A. (2008). Is there a need for fuzzy logic? Information Sciences, 178(13), 2751-2779.
• [7] Duricova, L., & Hromada, M. (2016). Fuzzy logic as support for security and safety solution in soft targets. MATEC Web of Conferences, 76, 02034. https://doi.org/10.1051/matecconf/20167602034
• [8] Ren, Q., Baron, L., & Balazinski, M. (2011). Type-2 fuzzy modeling for acoustic emission signal in precision manufacturing. Modelling and Simulation in Engineering, 2011, 1-12. https://doi.org/10.1155/2011/696947
• [9] Schneider, J., Bitterlich, N., Velcovsky, H., Morr, H., Katz, N., & Eigenbrodt, E. (2002). Fuzzy logic-based tumor-marker profiles improved sensitivity in the diagnosis of lung cancer. International Journal of Clinical Oncology, 7(3), 145-151. https://doi.org/10.1007/s101470200021
• [10] Schneider, J., Peltri, G., Bitterlich, N., Philipp, M., Velcovsky, H., Morr, H., … & Eigenbrodt, E. (2003). Fuzzy logic-based tumor marker profiles improved sensitivity of the detection of progression in small-cell lung cancer patients. Clinical and Experimental Medicine, 2(4), 185-191. https://doi.org/10.1007/s102380300005
• [11] Arani, L. A., Sadoughi, F., & Langarizadeh, M. (2019). An expert system to diagnose pneumonia using fuzzy logic. Acta Informatica Medica, 27(2), 103. https://doi.org/10.5455/aim.2019.27.103-107
• [12] Kumar, A., & Jha, P. (2022). Fuzzy logic applications in healthcare: A review-based study. In Next Generation Communication Networks for Industrial Internet of Things Systems (pp. 1-25).
• [13] Chang, T. C. (2023). A fuzzy evaluation approach to determine superiority of deep learning network system in terms of recognition capability: case study of lung cancer imaging. Annals of Operations Research, 1-21.
• [14] Black, W. C., Gareen, I. F., Soneji, S. S., et al. (2014). Computed tomography screening for lung cancer: How many false positive screens are acceptable? JAMA, 312(23), 2496-2497.
• [15] Rivera, M. M., Olvera-González, E., & Escalante, N. (2023). Upafuzzysystems: A python library for control and simulation with fuzzy inference systems. Machines, 11(5), 572.
• [16] Ahmadi, H., Gholamzadeh, M., Shahmoradi, L., Nilashi, M., & Rashvand, P. (2018). Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Computer Methods and Programs in Biomedicine, 161, 145-172.
• [17] Kumar, A., & Jha, P. (2022). Fuzzy logic applications in healthcare: A review-based study. In Next Generation Communication Networks for Industrial Internet of Things Systems (pp. 1-25).
• [18] Pandey, S. K., & Bhandari, A. K. (2023). A systematic review of modern approaches in healthcare systems for lung cancer detection and classification. Archives of Computational Methods in Engineering, 30, 4359-4378. https://doi.org/10.1007/s11831-023-09940-x
• [19] Liu, M., Wu, J., Wang, N., Zhang, X., Bai, Y., Guo, J., … & Tao, K. (2023). The value of artificial intelligence in the diagnosis of lung cancer: a systematic review and meta-analysis. Plos One, 18(3), e0273445. https://doi.org/10.1371/journal.pone.0273445
• [20] Daliri, M. R. (2012). A hybrid automatic system for the diagnosis of lung cancer based on genetic algorithm and fuzzy extreme learning machines. Journal of medical systems, 36, 1001-1005.
• [21] Lin, C. J., & Yang, T. Y. (2023). A fusion-based convolutional fuzzy neural network for lung cancer classification. International Journal of Fuzzy Systems, 25(2), 451-467.
• [22] Thomas, L. L., Goni, I. and Emeje G. D. (2019). Fuzzy Models Applied to Medical Diagnosis: A Systematic Review. Advances in Networks, 7(2), 45-50. https://doi.org/10.11648/j.net.20190702.15
• [23] Wagner, M., Schulze, A., Haselbeck-Köbler, M., Probst, P., Brandenburg, J. M., Kalkum, E., ... & Müller-Stich, B. P. (2022). Artificial intelligence for decision support in surgical oncology-a systematic review. Artificial Intelligence Surgery, 2(3), 159-172.
• [24] Jan, Z., El Assadi, F., Abd-Alrazaq, A., & Jithesh, P. V. (2023). Artificial intelligence for the prediction and early diagnosis of pancreatic cancer: Scoping review. Journal of Medical Internet Research, 25, e44248.
• [25] Croitoru, D., Huang, Y., Kurdina, A., Chan, A., & Drucker, A. M. (2019). Quality of reporting in systematic reviews published in dermatology journals. British Journal of Dermatology, 182(6), 1469-1476. https://doi.org/10.1111/bjd.18528
• [26] Majumder, S., Gautam, N., Basu, A., Sau, A., Geem, Z. W., & Sarkar, R. (2024). MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans. Plos one, 19(3), e0298527.
• [27] Kumar, S. J. N., Alam, M. G., Raj, T. M., & Mageswari, R. U. (2024). Golden Search Optimization based adaptive and diagonal kernel convolution neural network for disease prediction and securing IoT data in cloud. Applied Soft Computing, 151, 111137.
• [28] Lin, CJ, Lin, XQ ve Jhang, JY (2024). Akciğer Kanseri Görüntüleri için Karşıt Öğrenme Tabanlı Taguchi Evrişimli Bulanık Sinir Sınıflandırıcı. IEEE Erişimi .
• [29] Arun Shalin, L. V., Kalaivani, K., Vishal Ratansing, P., & Sathish Kumar, J. (2022). Prognostic Analysis and Early Prediction of Lung Cancer through Gene Analysis. Cybernetics and Systems, 1-17.
• [30] Jassim, M. M., & Jaber, M. M. (2022). Hybrid Selection Framework for Class Balancing Approaches Based on Integrated CNC and Decision Making Techniques for Lung Cancer Diagnosis. Eastern-European Journal of Enterprise Technologies, 118(9).
• [31] Sinthia, P., Malathi, M., K, A., & Suresh Anand, M. (2022). Improving lung cancer detection using faster region‐based convolutional neural network aided with fuzzy butterfly optimization algorithm. Concurrency and Computation: Practice and Experience, 34(27), e7251.
• [32] Wu, X., Denise, B. B., Zhan, F. B., & Zhang, J. (2022). Determining association between lung cancer mortality worldwide and risk factors using fuzzy inference modeling and random forest modeling. International Journal of Environmental Research and Public Health, 19(21), 14161.
• [33] Geetha, N. (2022). Deep Separable Convolution Network for Prediction of Lung Diseases from X-rays. International Journal of Advanced Computer Science and Applications, 13(6).
• [34] Dev, P. P., Patil, S. D., Hulipalled, V. R., & Patil, K. (2022). Fuzzy Sematic Segmentation and Efficient Classification of Lung Cancer Multi-Dimensional Datasets. International Journal of Fuzzy System Applications (IJFSA), 11(3), 1-12.
• [35] Hussain, L., Aziz, W., Alshdadi, A. A., Nadeem, M. S. A., & Khan, I. R. (2019). Analyzing the dynamics of lung cancer imaging data using refined fuzzy entropy methods by extracting different features. IEEE Access, 7, 64704-64721.
• [36] Arunkumar, C., & Ramakrishnan, S. (2019). Prediction of cancer using customised fuzzy rough machine learning approaches. Healthcare technology letters, 6(1), 13-18.
• [37] Polat, K., & Güneş, S. (2008). Principles component analysis, fuzzy weighting pre-processing and artificial immune recognition system based diagnostic system for diagnosis of lung cancer. Expert Systems with Applications, 34(1), 214-221.
• [38] Xing, J., Li, C., Wu, P., Cai, X., & Ouyang, J. (2024). Optimized fuzzy K-nearest neighbor approach for accurate lung cancer prediction based on radial endobronchial ultrasonography. Computers in Biology and Medicine, 171, 108038.
• [39] Zhang, M., Zhu, L., Sun, Y., Niu, D., & Liu, J. (2022). Computed tomography of ground glass nodule image based on fuzzy C-means clustering algorithm to predict invasion of pulmonary adenocarcinoma. Journal of Radiation Research and Applied Sciences, 15(1), 152-158
• [40] Nivedita, A. S., & Sharma, M. K. (2021). Fuzzy mathematical inference system and its application in the diagnosis of lung cancer. Int. J. Agric. Stat. Sci, 17(2), 709-717.
• [41] Zakaria, A. S., Shafi, M. A., Mohd Zim, M. A., & Musa, A. N. (2024). Comparison of prediction fuzzy modeling towards high-risk symptoms of lung cancer. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-10.
• [42] Manikandan, T., & Bharathi, N. (2017). Hybrid neuro-fuzzy system for prediction of stages of lung cancer based on the observed symptom values. Biomed Res, 28(2), 588-593.
• [43] Navaneethakrishnan, M., Anand, M. V., Vasavi, G., & Rani, V. V. (2023). Deep Fuzzy SegNet-based lung nodule segmentation and optimized deep learning for lung cancer detection. Pattern Analysis and Applications, 26(3), 1143-1159.
• [44] Khalil, A. M., Li, S. G., Lin, Y., Li, H. X., & Ma, S. G. (2020). A new expert system in prediction of lung cancer disease based on fuzzy soft sets. Soft Computing, 24(18), 14179-14207.
• [45] Singh, Y., & Susan, S. (2023). Lung cancer subtyping from gene expression data using general and enhanced fuzzy min–max neural networks. Engineering Reports, 5(11), e12663.
• [46] Ramkumar, M., Kalirajan, K., Kumar, U. P., & Surya, P. (2024). Deep volcanic residual U-Net for nodal metastasis (Nmet) identification from lung cancer. Biomedical Engineering Letters, 14(2), 221-233.
• [47] Zakaria, A. S., Shafi, M. A., Zim, M. A. M., & Razali, S. N. A. M. (2023). The Use of Fuzzy Linear Regression Modeling to Predict High-risk Symptoms of Lung Cancer in Malaysia. International Journal of Advanced Computer Science and Applications, 14(5).
• [48] Nan, Y., Del Ser, J., Tang, Z., Tang, P., Xing, X., Fang, Y., ... & Yang, G. (2023). Fuzzy attention neural network to tackle discontinuity in airway segmentation. IEEE Transactions on Neural Networks and Learning Systems.
• [49] Gugulothu, V. K., & Balaji, S. (2023). An automatic classification of pulmonary nodules for lung cancer diagnosis using novel LLXcepNN classifier. Journal of Cancer Research and Clinical Oncology, 149(9), 6049-6057.
• [50] Nagaraja, P., & Chennupati, S. K. (2023). Integration of adaptive segmentation with heuristic-aided novel ensemble-based deep learning model for lung cancer detection using CT images. Intelligent Decision Technologies, 17(4), 1135-1160.
• [51] Albert Jerome, S., Vijila Rani, K., Mithra, K. S., & Eugine Prince, M. (2023). Watershed segmentation with CAFIS and RCNN classification for pulmonary nodule detection. IETE Journal of Research, 69(8), 5052-5063.
• [52] Geng, M., Geng, M., Wei, R., & Chen, M. (2022). Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer. Journal of Thoracic Disease, 14(11), 4384.
• [53] Prasad, J., Chakravarty, S., & Krishna, M. V. (2022). Lung cancer detection using an integration of fuzzy K-means clustering and deep learning techniques for CT lung images. Bulletin of the Polish Academy of Sciences Technical Sciences, e139006-e139006.
• [54] Thamiselvan, P. (2022). Lung Cancer Examination and Risk Severity Prediction using Data Mining Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 10(3), 737-742.
• [55] Nivedita, A. S., & Sharma, M. K. (2021). Fuzzy mathematical inference system and its application in the diagnosis of lung cancer. Int. J. Agric. Stat. Sci, 17(2), 709-717.
• [56] Lavanya, M., Kannan, P. M., & Arivalagan, M. (2021). Lung cancer diagnosis and staging using firefly algorithm fuzzy C-means segmentation and support vector machine classification of lung nodules. International Journal of Biomedical Engineering and Technology, 37(2), 185-200.
• [57] Priyadharshini, A., & Chitra, S. (2022). A new systematic model for analysis and a hybrid fuzzy multimodality model for lung tumor prediction. Journal of Intelligent & Fuzzy Systems, 42(6), 5591-5604.
• [58] Deepa, P., & Suganthi, M. (2020). A fuzzy shape representation of a segmented vessel tree and kernel-induced random forest classifier for the efficient prediction of lung cancer. The Journal of Supercomputing, 76(8), 5801-5824.
• [59] Zhao, Z., Zhao, J., Song, K., Hussain, A., Du, Q., Dong, Y., ... & Yang, X. (2020). Joint DBN and Fuzzy C-Means unsupervised deep clustering for lung cancer patient stratification. Engineering Applications of Artificial Intelligence, 91, 103571.
• [60] Yazdani, H., Cheng, L. L., Christiani, D. C., & Yazdani, A. (2018). Bounded fuzzy possibilistic method reveals information about lung cancer through analysis of metabolomics. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(2), 526-535.
• [61] Liao, H., Long, Y., Tang, M., Streimikiene, D., & Lev, B. (2019). Early lung cancer screening using double normalization-based multi-aggregation (DNMA) and Delphi methods with hesitant fuzzy information. Computers & Industrial Engineering, 136, 453-463.
• [62] Moitra, D., & Mandal, R. K. (2019). Automated grading of non-small cell lung cancer by fuzzy rough nearest neighbour method. Network Modeling Analysis in Health Informatics and Bioinformatics, 8, 1-9.
• [63] Reddy, U., Reddy, B., & Reddy, B. (2019). Recognition of Lung Cancer Using Machine Learning Mechanisms with Fuzzy Neural Networks. Traitement du Signal, 36(1), 87-91.
• [64] Palani, D., & Venkatalakshmi, K. (2019). An IoT based predictive modelling for predicting lung cancer using fuzzy cluster based segmentation and classification. Journal of Medical Systems, 43, 1-12.
• [65] Arunkumar, C., & Ramakrishnan, S. (2019). Prediction of cancer using customised fuzzy rough machine learning approaches. Healthcare technology letters, 6(1), 13-18.
• [66] Yılmaz, A., Arı, S., & Kocabıçak, Ü. (2016). Risk analysis of lung cancer and effects of stress level on cancer risk through neuro-fuzzy model. Computer methods and programs in biomedicine, 137, 35-46.
• [67] Manikandan, T., & Bharathi, N. (2016). Lung cancer detection using fuzzy auto-seed cluster means morphological segmentation and SVM classifier. Journal of medical systems, 40, 1-9.
• [68] Sakthivel, K., Jayanthiladevi, A., & Kavitha, C. (2016). Automatic detection of lung cancer nodules by employing intelligent fuzzy c-means and support vector machine. BIOMEDICAL RESEARCH-INDIA, 27, S123-S127.
• [69] Ghosh, A., & De, R. K. (2016). Fuzzy Correlated Association Mining: Selecting altered associations among the genes, and some possible marker genes mediating certain cancers. Applied Soft Computing, 38, 587-605.
• [70] Phillips, M., Altorki, N., Austin, J. H., Cameron, R. B., Cataneo, R. N., Greenberg, J., ... & Schmitt, P. (2007). Prediction of lung cancer using volatile biomarkers in breath. Cancer biomarkers, 3(2), 95-109.
• [71] Turna, A., Mercan, C. A., & Bedirhan, M. A. (2005). Prediction of morbidity after lung resection in patients with lung cancer using fuzzy logic. The Thoracic and Cardiovascular Surgeon, 53(06), 368-374.
• [72] Page,M.J.,McKenzie,J.E.,Bossuyt,P.M.,Boutron,I.,Hoffmann,T.C.,Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan,S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A.,Lalu,M.M.,Li,T.,Loder,E.W.,Mayo-Wilson,E.,McDonald,S.,McGuinness, LA, & Moher, D. (2021). The PRISMA 2020 state-ment: An updated guideline for reporting systematic reviews. BMJ,372, n71. https://doi.org/10.1136/bmj.n71.