Comparative Analysis of Classical and Quantum Support Vector Machines for Diabetes Prediction Using PCA-Based Feature Reduction

Abstract

A chronic metabolic disease, diabetes mellitus has become a serious global public health concern. Using the Indian Pima Diabetes Dataset, this study compares Classical Support Vector Machines (CSVM) with Quantum Support Vector Machines (QSVM) for diabetes prediction. To address the challenges associated with high-dimensional data and feature redundancy, before classification, a feature reduction method called Principal Component Analysis (PCA) is used. Experimental results demonstrate that PCA effectively reduces computational complexity while preserving essential information required for classification. Furthermore, the study investigates the potential advantages of quantum-enhanced learning in healthcare prediction tasks by comparing the predictive capabilities and computational characteristics of QSVM against its classical counterpart. The findings provide insights into the applicability of quantum machine learning techniques for medical diagnosis and highlight the opportunities and challenges associated with implementing quantum algorithms on real-world healthcare datasets.