Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus

Abstract

Diabetes mellitus is a chronic disease and a major public health challenge worldwide. According to the International Diabetes Federation, there are currently 246 million diabetic people worldwide, and this number is expected to rise to 380 million by 2025. Furthermore, 3.8 million deaths are attributable to diabetes complications each year. It has been shown that 80% of type 2 diabetes complications can be prevented or delayed by early identification of people at risk. In this context, several data mining and machine learning methods have been used for the diagnosis, prognosis, and management of diabetes. In this paper, we propose utilizing support vector machines (SVMs) for the diagnosis of diabetes. In particular, we use an additional explanation module, which turns the "black box" model of an SVM into an intelligible representation of the SVM's diagnostic (classification) decision. Results on a real-life diabetes dataset show that intelligible SVMs provide a promising tool for the prediction of diabetes, where a comprehensible ruleset have been generated, with prediction accuracy of 94%, sensitivity of 93%, and specificity of 94%. Furthermore, the extracted rules are medically sound and agree with the outcome of relevant medical studies.

An Early Detection Method of Type-2 Diabetes Mellitus in Public Hospital

“…Classification accuracy (%) of each splitted feature, ROC curve, and area under curve are shown in Table 4, Figure 1, and Table 5, respectively. Although pattern can be extracted from SVMs as describe previously in [8], we extracted all patterns from Decision Tree (J48) since our limitation to get the source and also extracted pattern from SVMs is not already implemented in WEKA. There are 14 interesting patterns of 39 patterns, but not all patterns will be used.…”

“…Rule extraction from SVMs has been conducted by Barakat and Bradley [8], an experts system based on principal component analysis (PCA) and adaptive neuro-fuzzy inference systems, Polat and Gunes reported in [9]. In [10] Yu et al combined quantum particle swarm optimization (QPSO) and weighted least square (WLS-SVM) to diagnose type-2 of diabates.…”

GA_RBF NN: a classification system for diabetes

“…So, the physicians can perform very accurate decisions by using such an efficient tool. Barakat et al (2010) worked on the classification of diabetes disease using a machine learning approach such as Support Vector Machine (SVM). The paper implements a new and efficient technique for the classification of medical diabetes mellitus using SVM.…”

Classification of Regions with Endemic Diseases Based on Trace Element Concentrations in Groundwater

“…Recently support vector machines, developed by Vapnik [20], have been used for a range of problems including pattern recognition [21,22], bioinformatics [23,24], and text categorization [25,26]. The use of classification in this facet and in medical diagnosis has been gradually increasing [27][28][29][30]. SVM provides a novel approach for a two-variable classification problem [31].…”