Which algorithms are used in this heart failure prediction project?

The system implements and compares two algorithms: Decision Tree and Random Forest. Random Forest achieved superior performance with 87.5% accuracy and 0.914 ROC-AUC.

What is the accuracy of the heart failure risk prediction model?

The Random Forest model achieves 87.5% accuracy with an F1-score of 0.848 and ROC-AUC of 0.914, demonstrating excellent predictive capability for clinical risk assessment.

Who can benefit from this heart failure risk prediction system?

Healthcare providers, cardiologists, primary care physicians, clinical researchers, and hospital administrators can use this system for early screening, clinical decision support, and personalized patient care.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, and imbalanced-learn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Heart Failure Risk Prediction Using Machine Learning

The Heart Failure Risk Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts heart failure mortality risk using clinical patient data. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Decision Tree and Random Forest with SMOTE for class imbalance, achieving 87.5% accuracy and 0.914 ROC-AUC.

The system utilizes the Heart Failure Clinical Records Dataset containing 299 patient records with 12 clinical features including age, ejection fraction, serum creatinine, and serum sodium levels. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time heart failure risk prediction, enabling healthcare professionals to make informed clinical decisions and improve patient outcomes.

Python 3.8+ Machine Learning Random Forest Decision Tree Scikit-learn SMOTE Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Heart Failure Prediction
Random Forest (87.5% Accuracy)
Decision Tree (82.3% Accuracy)
Feature Engineering (Kidney Score, Heart Function Score)
Leak-free SMOTE Oversampling
Interactive Visualizations
Real-time Risk Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

🌳 Decision Tree

Interpretable tree-based classifier with max_depth=4, min_samples_split=20, class_weight='balanced'

🎯 Accuracy: 82.3%

🌲 Random Forest

Ensemble learning with n_estimators=50, max_depth=4, class_weight='balanced'

🎯 Accuracy: 87.5%

🔧 Feature Engineering

Kidney_Score, Heart_Function_Score, Risk_Score, Survival_Index

📊 Top Feature: Ejection Fraction

🔄 SMOTE Oversampling

Synthetic minority oversampling for class imbalance handling

📊 CV Mean: 87.1%

Methodology & Workflow

1 Data Collection
299 patient records with 12 clinical features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Decision Tree and Random Forest with SMOTE

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time risk prediction

Model Performance Comparison

Metric	Decision Tree	Random Forest	Best
Test Accuracy	82.3%	87.5%	Random Forest
Test Precision	78.9%	85.7%	Random Forest
Test Recall	74.2%	83.9%	Random Forest
Test F1-Score	76.5%	84.8%	Random Forest
Test ROC-AUC	84.2%	91.4%	Random Forest
CV Mean Accuracy	81.5%	87.1%	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Heart Failure Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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