Heart Disease Prediction

AI-powered cardiovascular risk assessment and clinical decision support system

Heart Disease Prediction Dashboard

Machine Learning model performance & clinical decision support system

Total Patients

303

Features

14

Positive Cases

165

Negative Cases

138

Clinical Risk Factors Analysis

Age Distribution

54.4 avg
Low Risk:< 45 years
High Risk:≥ 55 years

Cholesterol

246.3 avg
Low Risk:< 200 mg/dl
High Risk:≥ 240 mg/dl

Max Heart Rate

149.6 avg
Low Risk:≥ 150 bpm
High Risk:< 120 bpm

Resting BP

131.6 avg
Low Risk:< 130 mmHg
High Risk:≥ 140 mmHg

Project Overview

This machine learning-powered dashboard provides cardiovascular risk assessment using advanced algorithms trained on comprehensive patient datasets to support clinical decision-making.

The system integrates multiple risk factors and biomarkers to generate personalized risk scores with feature importance analysis for transparent clinical insights.

Key Features

  • • Real-time risk prediction
  • • Feature importance visualization
  • • Clinical recommendation engine
  • • Patient risk stratification

Technologies

  • • TensorFlow & Scikit-learn
  • • Random Forest & XGBoost
  • • Feature engineering pipelines
  • • Model validation & monitoring