Comparative Analysis of Machine Learning Algorithms for Diabetes Prediction: Finding the Optimal Approach

Abstract

Diabetes, as a chronic disease, poses a rapidly escalating risk to human health, stemming from a complex interplay of factors such as obesity, elevated blood glucose levels, and various other triggers. Central to its onset is the disruption of insulin hormone function, resulting in abnormal metabolism and increased blood sugar levels. In this paper, we propose a solution to this pressing issue using machine learning techniques. By applying various machine learning algorithms on the Pima Indian diabetes (PID) dataset, we aim to identify the most effective algorithm for this task. Leveraging powerful machine learning algorithms such as (SVM) Support Vector Machine, (RF) Random Forest and others, we endeavor to forecast the onset of diabetes. Through the amalgamation of these techniques, our objective is to proactively identify individuals at risk, enabling timely intervention and preventive measures to safeguard health. The primary goal of this initiative is to mitigate the risk of diabetes onset by forecasting individuals' susceptibility and advocating for lifestyle and dietary adjustments. This study has dual objectives: firstly, to develop and implement a predictive model for diabetes using machine learning techniques, and secondly, to explore effective strategies for achieving success in this endeavor.