A-Comparative-Machine-Learning-Framework-for-Heart-Attack-Risk-Prediction-Using-Data-Preprocessing
Compare machine learning models for clinical risk prediction.
Comparative ML pipeline featuring SMOTE, cross-validation, and multi-model evaluation.
This open-source Python framework compares supervised machine learning algorithms to predict heart attack risk using patient demographic, lifestyle, and clinical data. It suits data scientists and healthcare researchers building predictive diagnostic models.
What it is
It ingests patient records, handles class imbalance via SMOTE and class weighting, performs hyperparameter optimization, and runs five-fold cross-validation. You evaluate models across metrics like accuracy, recall, F1-score, and ROC-AUC rather than relying on accuracy alone.
Who it's for
It suits data scientists and healthcare analysts developing clinical risk prediction models in Python. It does not suit non-technical clinicians or teams needing a production-ready web application out of the box.
Key features
Multi-Model Comparison
Evaluates Logistic Regression, Random Forest, Decision Tree, Gradient Boosting, LightGBM, AdaBoost, and MLP Neural Networks.
Class Imbalance Handling
Applies SMOTE and class weighting to improve minority-class detection for high-risk cardiac events.
Hyperparameter Optimization
Tunes model configurations alongside five-fold cross-validation to reduce overfitting.
Comprehensive Metrics
Measures performance using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrices.
Up and running in 3 steps
Our honest verdict
What's great
- Compares seven distinct supervised machine learning models in a single framework.
- Includes explicit steps for handling class imbalance with SMOTE.
- Applies five-fold cross-validation to guard against overfitting.
- Evaluates clinical performance beyond basic accuracy using recall and F1-score.
Where it falls short
- Lacks a stated open source license in the repository metadata.
- Requires writing and executing Python code inside Jupyter Notebooks.
- Does not include a production web deployment interface or API endpoint.
Pricing & license
The code is available for download, though the repository does not state a specific open source license. There are no paid hosted options mentioned in the README, so the only cost is your time running it.
Open remote AI-training roles
874 open remote AI-training roles, updated November 15, 2026; most pay $20–$170 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
Use it, or have us set it up
FAQ
What dataset does the framework use?
It uses a Heart Attack Risk Prediction Dataset obtained from Kaggle containing demographic, physiological, and clinical variables.
Which machine learning algorithms are compared?
The framework compares Logistic Regression, Random Forest, Decision Tree, Gradient Boosting, LightGBM, AdaBoost, and MLP Neural Networks.
How does the framework handle class imbalance?
It uses the Synthetic Minority Oversampling Technique (SMOTE) and class weighting during the training process.
