A Metaheuristic for Fuzzy Density Based SVM and Confidence SMOTE for Early Prediction of Diabetes

Abstract

Early detection of diabetes, based on observable features, plays a crucial role in preventing serious complications in diabetic patients. In this study, we propose a classification model called SMOTE Density Based Fuzzy Support Vector Machine (SMOTE-DB-FSVM), based on FSVM, to better detect diabetes. Our approach is based on five main steps: data cleaning, density-based filtering, feature selection to identify the most important attributes, calculation of a confidence score for each point in the minority class, and use of SMOTE to balance the data. In addition, we compare different versions of the kernel functions in the SVM model to optimize classification results, using metaheuristics to estimate the parameters of these kernels. The proposed SMOTE-DB-FSVM algorithm has been evaluated in diabetes datasets, including the PIMA diabetes database, and the results show a clear improvement in the early detection of diabetes with this method.