Ai Cardiologist: Advancements In Supervised Learning For Heart Disease Prediction

Abstract

Cardiovascular Disease (CVD) Continues To Pose A Significant Global Health Challenge, Demanding Innovative Approaches For Early Detection And Prevention. This Paper Presents An AI Cardiologist System That Leverages Supervised Machine Learning Techniques To Predict Heart Disease With High Accuracy. The Proposed System Integrates A Bagging Classifier Ensemble Method Alongside A LeNet Convolutional Neural Network Architecture To Analyse Multi-dimensional Patient Data—including Demographics, Clinical History, Laboratory Results, And ECG Readings. The System Is Deployed As A Full-stack Web Application Using The Django Framework, Enabling Clinicians To Receive Real-time, Personalised Risk Assessments. Experiments Conducted On The UCI Heart Disease (CARDIO) Dataset Demonstrate Competitive Accuracy. Future Directions Include Integration Of Explainable AI (XAI) And Federated Learning To Enhance Transparency And Privacy Preservation.

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