Which algorithms are used in this stroke risk prediction project?

The system implements and compares two classification algorithms: Random Forest and Logistic Regression. Random Forest achieved the best performance with 94.8% accuracy and an F1-score of 0.927.

What features are used for stroke risk prediction?

The system uses features including age, gender, hypertension, heart disease, ever married, work type, residence type, glucose level, BMI, smoking status, and engineered features like age risk, glucose risk, BMI risk, and a combined risk index.

What is the accuracy of this stroke risk prediction model?

The Random Forest model achieves 94.8% test accuracy with precision of 94.2%, recall of 94.8%, F1-score of 0.927, and ROC-AUC of 0.937, making it highly reliable for clinical decision support.

Who can benefit from this stroke risk prediction system?

Healthcare providers can use it for early risk assessment, primary care physicians for patient screening, neurologists for referral prioritization, and patients for understanding their stroke risk factors.

What technologies are used to build this project?

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

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Machine Learning

Stroke Risk Prediction Using Machine Learning

The Stroke Risk Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts stroke risk using demographic, medical history, and lifestyle factors. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced algorithms like Random Forest and Logistic Regression with SMOTE to achieve 94.8% accuracy.

The system leverages features including age, gender, hypertension, heart disease, BMI, glucose level, smoking status, and engineered risk scores. It provides interactive visualizations, feature importance analysis, model comparison, and real-time stroke risk prediction to help healthcare providers identify high-risk patients early for preventive care.

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

Key Features:

Binary Stroke Risk Prediction
Random Forest (94.8% Accuracy)
Logistic Regression (92.1% Accuracy)
SMOTE for Class Imbalance
Feature Importance Analysis
Real-time Risk Prediction
Model Performance Comparison
Interactive Visualizations
5-Fold Stratified CV
Flask Web Application

Algorithms Used

🌲 Random Forest Classifier

Ensemble learning with 300 trees, captures non-linear relationships effectively

🎯 Accuracy: 94.8%

📊 Logistic Regression

Statistical model with L2 regularization, interpretable coefficients

🎯 Accuracy: 92.1%

🔧 Feature Engineering

Age risk, glucose risk, BMI risk, combined risk index

📊 Features: 15+

🔍 5-Fold Leak-Free CV

Cross-validation with SMOTE inside each fold

📊 RF: 93.4% ± 1.8%

Methodology & Workflow

1 Data Loading & Inspection
Healthcare dataset with 5,110 patient records

2 Data Preprocessing
Cleaning, encoding, missing value imputation, feature scaling

3 Feature Engineering
Age risk, glucose risk, BMI risk, combined risk index

4 Model Training
Random Forest & Logistic Regression with SMOTE pipelines

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

6 Web Deployment
Flask web app for real-time stroke risk prediction

Model Performance Comparison

Metric	Random Forest	Logistic Regression	Best
Accuracy	0.948	0.921	Random Forest
Precision	0.942	0.915	Random Forest
Recall	0.948	0.921	Random Forest
F1-Score	0.927	0.893	Random Forest
ROC-AUC	0.937	0.895	Random Forest
CV Mean (5-Fold)	0.934	0.891	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (5,110 records) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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Complete Source Code

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