Prediction of Diabetes Using Supervised Machine Learning Algorithms

Diabetes mellitus is one of the most common chronic conditions across the globe, and if it isn't detected early, it can lead to serious health complications.With the growing use of machine learning (ML), healthcare professionals are increasingly relying on predictive models to help identify and diagnose diabetes at an earlier stage.This paper provides an in-depth review of several supervised machine learning algorithms used for predicting diabetes.It looks at widely used methods such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN).The performance of these models is compared using key evaluation metrics like accuracy, precision, recall, and F1-score, often tested on standard datasets such as the PIMA Indian Diabetes Dataset.In addition, the paper discusses current challenges in diabetes prediction and explores potential directions for future research.