Early Detection of Diabetes Project Guide

Diabetes is a rapidly growing health concern worldwide, often going undiagnosed until complications arise. Early prediction can significantly help in managing and treating the disease effectively. Traditional diagnostic methods are time-consuming and require frequent checkups.

By utilizing Machine Learning, we can develop a predictive system that analyzes patient data, detects patterns, and forecasts the likelihood of diabetes early — enabling patients to take preventive measures sooner.

Here's the step-by-step working process for building a reliable diabetes prediction system:

• Collect health datasets like PIMA Indian Diabetes dataset.
• Preprocess the data: handle missing values, normalize data, feature selection.
• Train Machine Learning models to classify whether a patient is diabetic or non-diabetic.
• Evaluate model performance using accuracy, ROC-AUC, and confusion matrix.
• Deploy the model into a web or mobile app for public use.

• Scikit-Learn Documentation
• TensorFlow Documentation
• Keras Documentation
• PIMA Indian Diabetes Dataset on Kaggle