Abstract :

Diabetes is a chronic disease that affects millions of people worldwide and requires early detection for effective management. Traditional diagnostic methods often depend on manual medical tests and clinical expertise, which may delay timely prediction. This project presents a Diabetes Prediction System using Machine Learning to identify the risk of diabetes at an early stage. The system analyses medical parameters such as glucose level, blood pressure, BMI, insulin, and age. Machine learning algorithms are trained on historical health datasets to classify diabetic and non-diabetic cases. The proposed model improves prediction accuracy and reduces human error. Automation enables fast and reliable diagnosis support. The system assists healthcare professionals in decision making. It provides cost-effective and scalable screening solutions. The results show improved accuracy compared to traditional methods. This project demonstrates the effectiveness of machine learning in healthcare pred