Performance Evaluation of Different Artificial Neural Network Models in the Classification of Type 2 Diabetes Mellitus

Abstract

Objective: In this study, it is aimed to classify type 2 Diabetes Mellitus (DM), compare the estimates of the Artificial Neural Network models and determine the factors related to the disease by applying Multilayer Perceptron (MLP) and Radial Based Function (RBF) methods on the open-access dataset. Material and Methods: In this study, the data set named “Pima Indians Diabetes Database” was obtained from https://www.kaggle.com/uciml/pima-indians-diabetes-database. The dataset contains 768 records with 268 (34.9%) type 2 diabetes patients and 500 (65.1%) people without diabetes, which have 9 variables (8 inputs and 1 outcome). MLP and RBF methods, which are artificial neural network models, were used to classify type 2 DM. Factors associated with type 2 DM were estimated by using artificial neural network models. Results: The performance values obtained with MLP from the applied models were accuracy 78.1%, specificity 81.2%, AUC 0.848, sensitivity 71%, positive predictive value 61.7%, negative predictive value 86.8% and F-score 66%. In relation to RBF model, the performance metrics were accuracy obtained 76.8%, specificity 82.1%, AUC 0.813, sensitivity 66.0%, positive predictive value 64.6%, negative predictive value 83% and F-score 65.3%, respectively. When the effects of the variables in the data set examined in this study on Type 2 DM are analyzed; The three most important variables for the MLP model were obtained as Glucose, BMI, Pregnancies respectively. For RBF, it was obtained as Glucose, Skin Thickness, and Insulin. Conclusion: The findings obtained from this study showed that the models used gave successful predictions for Type 2 DM classification. Besides, unlike similar studies examining the same dataset, the significance values of the factors associated with the models created were estimated.

Keywords

• Classification,Multilayer perceptron neural network,Radial-based function neural network,Type 2 Diabetes Mellitus

References

1. [1] J. A. Cramer, "A systematic review of adherence with medications for diabetes," Diabetes care, vol. 27, no. 5, pp. 1218-1224, 2004. [2] R. Shobhana, R. Begum, C. Snehalatha, V. Vijay, and A. Ramachandran, "Patients' adherence to diabetes treatment," The Journal of the Association of Physicians of India, vol. 47, no. 12, pp. 1173-1175, 1999. [3] N. Başkal, "Diabetes Mellitus Tanım, Klasifikasyon, Tanı, Klinik, Laboratuar ve Patogenez," Erdoğan G. Klinik Endokrinoloji. Anıtıp AŞ yayınları, Ankara, pp. 207-233, 2003. [4] A. Cameron, "The metabolic syndrome: validity and utility of clinical definitions for cardiovascular disease and diabetes risk prediction," Maturitas, vol. 65, no. 2, pp. 117-121, 2010. [5] M. Arslan, "Diabetes mellitusta tanı ve sınıflandırma," İliçin G, Biberoğlu K, Süleymanlar G, Ünal S (editörler). İç Hastalıkları, vol. 2, pp. 2279-2295, 2003. [6] T. Yılmaz, "Diabetes mellitusun tanı kriterleri ve sınıflaması," Diabetes Mellitus’ un Modern Tedavisi, birinci baskı, İstanbul, Türkiye Diyabet Vakfı, 2003. [7] E. Öztemel, "Yapay Sinir Ağları, Papatya Yayıncılık, 2," Baskı, İstanbul, pp. 29-57, 2006. [8] S. Haykin, "Neural Networks: A comprehensive Foundation. by Prentice-Hall, Inc," Upper Saddle River, New Jersey, vol. 7458, pp. 161-175, 1999. [9] H. Batar, "EEG işaretlerinin dalgacık analiz yöntemleri kullanılarak yapay sinir ağları ile sınıflandırılması," Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, Kahramanmaraş, 89s, 2005. [10] A. Arı and M. E. Berberler, "Yapay Sinir Ağları ile Tahmin ve Sınıflandırma Problemlerinin Çözümü İçin Arayüz Tasarımı," Acta Infologica, vol. 1, no. 2, pp. 55-73, 2017. [11] E. Kilic, U. Ozbalci, and H. Ozcalik, "Lineer Olmayan Dinamik Sistemlerin Yapay Sinir Ağları ile Modellenmesinde MLP ve RBF Yapılarının Karşılaştırılması," ELECO2012 Elektrik-Elektronik ve Bilgisayar Mühendisliği Sempozyomu,(29.11. 2012-01.12. 2012), 2012. [12] S. S. Haykin, "Neural networks and learning machines/Simon Haykin," ed: New York: Prentice Hall, 2009. [13] M. Barale and D. J. I. J. o. C. A. Shirke, "Cascaded modeling for PIMA Indian diabetes data," vol. 139, no. 11, pp. 1-4, 2016. [14] J. W. Smith, J. Everhart, W. Dickson, W. Knowler, and R. Johannes, "Using the ADAP learning algorithm to forecast the onset of diabetes mellitus," in Proceedings of the Annual Symposium on Computer Application in Medical Care, 1988, p. 261: American Medical Informatics Association. [15] U. Orhan, M. Hekim, and M. Özer, "Discretization approach to EEG signal classification using Multilayer Perceptron Neural Network model," in 2010 15th National Biomedical Engineering Meeting, 2010, pp. 1-4: IEEE. [16] O. Kaynar, Y. Görmez, Y. E. Işık, and F. Demirkoparan, "Değişik Kümeleme Algoritmalarıyla Eğitilmiş Radyal Tabanlı Yapay Sinir Ağlarıyla Saldırı Tespiti," in International Artificial Intelligence and Data Processing Symposium (IDAP'16), 2016. [17] I. M. Nasser and S. S. Abu-Naser, "Lung Cancer Detection Using Artificial Neural Network," International Journal of Engineering and Information Systems (IJEAIS), vol. 3, no. 3, pp. 17-23, 2019. [18] V. A. Kumari and R. Chitra, "Classification of diabetes disease using support vector machine," International Journal of Engineering Research and Applications, vol. 3, no. 2, pp. 1797-1801, 2013. [19] F. Mercaldo, V. Nardone, and A. Santone, "Diabetes mellitus affected patients classification and diagnosis through machine learning techniques," Procedia computer science, vol. 112, pp. 2519-2528, 2017.