UTILIZING A FUSION OF MACHINE LEARNING TECHNIQUES FOR DIABETES MELLITUS SUBTYPES CLASSIFICATION AND IDENTIFICATION
Abstract
Diabetes Mellitus (DM) is one of the most common health challenges in the world we live in today. It is a deadly disease which prevents the body from making enough insulin. Diabetes Type1 and Type2 are the two major types, which have some similarity in symptoms. Identifying Diabetic Patients with respect to type plays a very significant role in the management process. Misdiagnosis of these types leads to serious impediments. Research shows that the overlapping nature of features contributed to the difficulty in identifying the types and the classification into sub-types. This is still an area of concern (Hassan, et al, 2020; Albahli, 2020). In this research, we proposed a method of Support Vector Machine (SVM) and Random Forest Tree (RFT) for the classification of Diabetes sub-types. To reduce the dimensions of the feature set, the Principal Component Analysis (PCA) and Logistic Regression (LR) were used. For effective research, data is sourced from the Center for Endocrinology and Diabetes-Al-Kindy Teaching Hospital and Medical City Hospital's public laboratory Dataset to ensure wide coverage. The dataset consists of 834 patient records with eight features and an output column labelled "Type I" or "Type II." This study conducted the experiment using Python, and the results show that the hybrid model outperformed the other prediction methods.
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