Third year university IoT module for monitoring heart health in diabetic patients. The system combines hardware sensors, real-time data processing, and machine learning analytics to detect anomalies and track vital sign trends. The results are then displayed in a Streamlit web page.

A smart, AI-powered tool designed to assist in cardiovascular health management through personalized insights and reminders.

Aplikasi Flask ini menggunakan machine learning untuk memprediksi risiko serangan jantung dari data klinis pasien melalui REST API.

This ML project predicts heart disease using logistic regression on the Cleveland Heart Disease UCI dataset, featuring advanced preprocessing and medical feature engineering, achieving 82.1% accuracy with strong cross-validation.

End-to-end AI Heart Diagnostic Suite with a benchmarked ML core and an interactive MERN-stack clinical portal.