Diabetes Prediction Based on Machine Learning

Abstract

Diabetes mellitus is a growing global health issue with an increasing incidence rate. Traditional methods for predicting diabetes, which rely heavily on clinical data and physical examinations, present challenges such as high costs, time-consuming processes, and difficulties in providing timely and personalized risk assessments. However, with the rise of machine learning (ML), new opportunities have emerged in diabetes prediction, utilizing large-scale data and advanced pattern recognition techniques. This study examines the application of ML in diabetes risk assessment by leveraging electronic health records (EHR) and big data, leading to significant improvements in accuracy and efficiency. The results demonstrate that ML methods can more effectively identify high-risk individuals, facilitating early intervention and contributing to the advancement of diabetes prediction.