Keywords
How to Cite
Abstract
Background: The development of data mining techniques and the adaptive neuro-fuzzy inference system (ANFIS) in the last few decades has made it possible to achieve accurate predictions in medical fields. The present study aimed to use the ANFIS model, artificial neural network (ANN), and logistic regression to predict thyroid patients.
Methods: This study aimed to predict thyroid disease using the UCI database, ANFIS and ANN models, and logistic regression. We only used four of its features as the input of the model and considered thyroid as a binary response (occurrence=1, non-occurrence=0) as the output of the model. Finally, three models were compared based on the accuracy and the area under the curve (AUC).
Results: In this study, out of the extensive UCI database, which includes 3772 samples and over 20 features, only five specific features were utilized. Data include 1,144 males and 2,485 females. The results of multiple logistic regression analysis demonstrated that free T4 index (FTI) and thyroid stimulating hormone(TSH) had a significant effect on thyroid. The ANFIS model had a higher accuracy (99%) compared to ANN (94%) and the logistic regression model (93%) in the prediction of thyroid.
Conclusion: As evidenced by the obtained results, the forecasting performance of ANFIS is more efficient than other models. Moreover, the use of combined methods, such as ANFIS, to diagnose and predict diseases increases the accuracy of the model. Therefore, the results of this study can be used for screening programs to identify people at risk of thyroid disease.
References
Amrollahi Biyouki S, Türksen I, Fazel Zarandi M, editors. Fuzzy rule-based expert system for diagnosis of thyroid disease. IEEE International Conference on Fuzzy Systems Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) 2015 IE Conference Niagara Falls, ON, Canada; 2015.
Dogantekin E, Dogantekin A, Avci D. An expert system based on generalized discriminant analysis and wavelet support vector machine for diagnosis of thyroid diseases. Expert Systems with Applications. 2011;38(1):146-50.
Sargent DJ. Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer: Interdisciplinary International Journal of the American Cancer Society. 2001;91(S8):1636-42.
Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonça A. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity, and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees, and random forests. BMC research notes. 2011;4(1):1-14.
Biglarian A, Bakhshi E, Rahgozar M, Karimloo M. Comparison of artificial neural network and logistic regression in predicting of binary response for medical data: the stage of disease in gastric cancer(Persian). 2011.
Senol C, Yildirim T, editors. Thyroid and breast cancer disease diagnosis using fuzzy-neural networks. 2009 International Conference on Electrical and Electronics Engineering-ELECO 2009; 2009: IEEE.
Das R, Turkoglu I, Sengur A. Effective diagnosis of heart disease through neural networks ensembles. Expert systems with applications. 2009;36(4):7675-80.
Nouei MT, Kamyad AV, Sarzaeem M, Ghazalbash S. Fuzzy risk assessment of mortality after coronary surgery using a combination of adaptive neuro‐fuzzy inference system and K‐means clustering. Expert Systems. 2016;33(3):230-8.
Polat K, Şahan S, Güneş S. Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbor) based weighting preprocessing. Expert Systems with Applications. 2007;32(2):625-31.
Krueger E, Prior S, Kurtener D, Rogers H, Runion G. Characterizing root distribution with adaptive neuro-fuzzy analysis. International Agrophysics. 2011;25(1).
Dehdar Karsidani S, Farhadian M, Mahjub H, Mozayanimonfared A. Intelligent prediction of major adverse cardiovascular events (MACCE) following percutaneous coronary intervention using ANFIS-PSO model. BMC Cardiovascular Disorders. 2022;22(1):1-8.
Dua D, Graff C. UCI Machine Learning Repository. The University of California, School of Information and Computer Science, Irvine, CA (2019).
http://archive.ics.uci.edu/ml/datasets/Thyroid+Disease.
Borzouei S, Mahjub H, Sajadi NA, Farhadian M. Diagnosing thyroid disorders: Comparison of logistic regression and neural network models. Journal of Family Medicine and Primary Care. 2020;9(3):1470.
Hastie T, Tibshirani R, Friedman JH, Friedman JH. The elements of statistical learning: data mining, inference, and prediction: Springer; 2009.
Jang J-SR, Sun C-T, Mizutani E. Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on automatic control. 1997;42(10):1482-4.
Muthuvijayalaksh M. CLASSIFICATION OF TB DISEA. 2014.
Ahmad W, Huang L, Ahmad A, Shah F, Iqbal A, Saeed A. Thyroid diseases forecasting using a hybrid decision support system based on ANFIS, k-NN, and information gain method. J Appl Environ Biol Sci. 2017;7(10):78-85.
Zabbah I, Yasrebi Naeini SE, Ramazanpoor Z, Sahragard K. The diagnosis of thyroid diseases using a combination of neural networks through a hierarchical method. Journal of Health and Biomedical Informatics. 2017;4(1):21-31.
Azar AT, El-Said SA, Hassanien AE. Fuzzy and hard clustering analysis for thyroid disease. Computer methods and programs in biomedicine. 2013;111(1):1-16.
Hameed MA. Artificial neural network system for thyroid diagnosis. Journal of Babylon University. 2017;25(2):518-28.
Rastogi AB, Monika A. Study of neural network in the diagnosis of the thyroid. International Journal of Computer Technology and Electronics Engineering (IJCTEE) Vol. 2014;4.
Temurtas F. A comparative study on thyroid disease diagnosis using neural networks. Expert Systems with Applications. 2009;36(1):944-9.