Diabetic Prediction Using Machine Learning

Diabetes mellitus is one of the most prevalent chronic diseases globally, leading to severe health complications if not diagnosed early. With the advancement of data analytics and artificial intelligence, machine learning (ML) models have proven effective in medical diagnosis and disease prediction. This paper presents a diabetes prediction system using machine learning algorithms on the Pima Indians Diabetes Dataset. The study compares models such as Logistic Regression, Random Forest, and Support Vector Machine (SVM) to determine the most efficient model for accurate diagnosis. Experimental results show that Logistic Regression achieves the highest accuracy of 83%, indicating its robustness for binary classification tasks in healthcare analytics.