Utilizing Machine Learning Techniques in Diabetes Detection

Abstract

Machine learning, a significant field within computer science, offers tools for developing models that can make accurate predictions and assist in decision-making processes. This study investigates the application of machine learning to predict diabetes in patients based on health metrics such as glucose levels, blood pressure, and body mass index (BMI). A logistic regression model, a statistical method suitable for binary classification tasks, was implemented to determine the likelihood of diabetes. The model was trained and tested using patient health data, and its performance was evaluated in predicting diabetic versus non-diabetic cases, achieving a high level of accuracy. The findings demonstrate the effective- ness of machine learning in improving diagnostic precision and supporting healthcare decision-making. This approach has potential benefits for early diabetes detection and personalized treatment, illustrating how machine learning could be applied effectively in healthcare.