Diabetes Prediction Using Machine Learning

Abstract

“Diabetes is a widespread chronic condition that affects millions of people throughout the world”. Diabetes risk identification and prediction are critical in preventing its onset and effectively managing the condition. In this study, we employ machine learning techniques to develop a predictive model for diabetes risk assessment. We leverage a comprehensive dataset of patient information, including demographic, clinical, and lifestyle factors, to train and evaluate our model. Our results demonstrate the potential of machine learning in accurately predicting diabetes
risk, thus enabling timely interventions and Healthcare strategies that are tailored to the individual. This study adds to the increasing field of digital health and data-driven approaches to combating the diabetes epidemic. Utilizing a dataset comprising various health parameters and medical history, we develop predictive models to assess the likelihood of diabetes onset. Our research aims to enhance early diagnosis and intervention for diabetes, potentially reducing its impact on public health. We assess the performance of several machine learning algorithms and present promising results, demonstrating the potential for accurate and timely diabetes prediction.