Submit your papersSubmit Now
For Enquiries: [email protected]
IIARD LogoIIARD

Comparative Analysis of Decision Tree and Neural Network Models for Diabetes Type Classification Based on Clinical Symptoms

Chinonso Michael Eze, Jophet Ewere Okoh, Joel Eviano Israel, Eberechukwu Faith Onwumelu

Abstract

Accurately diagnosing and classifying diabetes types based on clinical symptoms remains a significant challenge in healthcare, particularly in designing effective treatment and control strategies. This study applies decision tree and neural network classification models to categorize diabetes types using symptom-based data obtained from medical records at All Saints’ Hospital, Owerri, Imo State, Nigeria (2024). While the decision tree model employs a rule-based approach to identify key predictors, the neural network leverages its capacity to model complex, non-linear relationships. The values of the average squared classification error and the correct classification rate indicate that the neural network model performs better than the decision tree model. The neural network model achieved a CCR of 31 and an ASCE of 32.6, while the decision tree model recorded a CCR of 27.66 and an ASCE of 88.33. Thus, the neural network model is identified as the superior approach for symptom- based diabetes classification in this context.

Keywords

Diabetes; classification; decision tree; neural networks

References

[1] American Diabetes Association, 'Diagnosis and classification of diabetes mellitus,' Diabetes Care, vol. 37, no. Suppl. 1, pp. S81–S90, 2014, doi: 10.2337/dc14-S081. [2] World Health Organization, 'Diabetes: Fact sheet 312,' 2019. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/diabetes [3] R. C. Tripathi and A. K. Srivastava, Diabetes Mellitus: Complications and Management. New Delhi, India: Jaypee Brothers Medical Publishers, 2006. [4] P. Dandona and S. Dhindsa, “Update: Hypogonadotropic hypogonadism in type 2 diabetes and obesity,” The Journal of Clinical Endocrinology & Metabolism, vol. 96, no. 9, pp. 2643–2651, 2011. [5] N. Chaudhary and N. Tyagi, 'Diabetes mellitus: An overview,' Int. J. Res. Dev. Pharm. Life Sci., vol. 7, no. 4, pp. 3023–3026, 2018, doi: 10.13040/IJRDPL.2278- 0238.7(4).3023-3026. [6] M. A. Atkinson and G. S. Eisenbarth, 'Type 1 diabetes: New perspectives on disease pathogenesis and treatment,' Lancet, vol. 358, no. 9277, pp. 221–229, 2001. [7] R. A. DeFronzo et al., 'Type 2 diabetes: A review of the current state of the art,' J. Clin. Endocrinol. Metab., vol. 100, no. 11, pp. 3929–3943, 2015. [8] B. E. Metzger, 'International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy,' Diabetes Care, vol. 33, no. 3, pp. 676–682, 2010. [9] P. M. Catalano et al., 'The hyperglycemia and adverse pregnancy outcome study: Paving the way for new diagnostic criteria for gestational diabetes mellitus,' Amer. J. Obstet. Gynecol., vol. 206, no. 4, pp. 257–258, 2012. [10] S. E. Kahn, 'Beta cell failure: Causes and consequences,' Int. J. Clin. Pract. Suppl., vol. 13, pp. 13–18, 2001. [11] L. Bellamy, J. P. Casas, A. D. Hingorani, and D. Williams, 'Type 2 diabetes mellitus after gestational diabetes: A systematic review and meta-analysis,' Lancet, vol. 373, no. 9677, pp. 1773–1779, 2009. [12] M. Conroy, P. Beer, C. Hughes, L. Phelan, and B. J. Bain, 'The frequency of detection of unexpected diabetes mellitus during haemoglobinopathy investigations,' J. Clin. Pathol., vol. 64, pp. 898–900, 2011. [13] Y. Zhang, Z. Lin, Y. Kang, R. Ning, and Y. Meng, 'Neural network-based approach for predicting diabetes types,' J. Healthcare Eng., vol. 2018, pp. 1–11, 2018, doi: 10.1155/2018/2757. [14] L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, 'Classification and regression trees,' Cytometry, vol. 8, no. 5, pp. 534–535, 1987. [15] J. R. Quinlan, 'Improved use of continuous attributes in C4.5,' J. Artif. Intell. Res., vol. 4, pp. 77–90, 1996. [16] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, 'Learning representations by back- propagating errors,' Nature, vol. 323, no. 6088, pp. 533–536, 1986. [17] Z. Xie, O. Nikolayeva, J. Luo, and D. Li, “Building risk prediction models for type 2 diabetes using machine learning techniques,” Preventing Chronic Disease, vol. 16, no. E130, Sep. 2019, doi: 10.5888/pcd16.190109. [18] H. Al-Rimmawi, 'Prediction of Type 2 Diabetes using logistic regression techniques: Prediction of Type 2 Diabetes,' Turkish J. Comput. Math. Educ. (TURCOMAT), vol. 15, 2024, doi: 10.61841/turcomat.v15i1.13875. [19] Karnika, “Decision tree-based approach for predicting type 2 diabetes,” Journal of Healthcare Engineering, pp. 1–9, 2019, doi: 10.1155/2017/3734982. E- ISSN 2489-009X , [20] P. Rajendra and S. Latifi, “Prediction of diabetes using logistic regression and ensemble techniques,” Computers in Biology and Medicine, vol. 137, p. 10032, 2021, doi: 10.1016/j.cmpbup.2021.10032. [21] C. M. Eze, F. I. Ugwuowo, and O. Asogwa, 'A comparative analysis of vector autoregressive model and neural networks,' Int. J. Math. Stat., vol. 4, no. 8, pp. 1–13, 2018. [22] O. C. Asogwa and A. V. Oladugba, 'Of students academic performance rates using artificial neural networks ,' Amer. J. Appl. Math. Stat., vol. 3, no. 4, pp. 151– 155, 2015. [23] G. Cybenko, 'Approximation by superpositions of a sigmoidal function,' Math. Control Signals Syst., vol. 2, no. 4, pp. 303–314, 1989. [24] S. G. Ritchie and C. Oh, 'Recognizing vehicle classification information from blade sensor signature,' Pattern Recognit. Lett., vol. 28, no. 9, pp. 1041–1049, 2007. [25] E. A. El-Sebakhy, A. S. Hadi, and K. A. Faisal, 'Iterative least squares functional networks classifier,' IEEE Trans. Neural Netw., vol. 18, no. 3, pp. 844–850, 2007.