HeartRisk AI: Interpretable CVD Prediction Tool

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Cardiovascular disease is the leading cause of death, but many cases can be prevented with early detection. We built HeartGuard to estimate heart disease risk using basic health data and show how simple machine learning can support preventive healthcare. What it does HeartGuard predicts cardiovascular disease risk from clinical and lifestyle features. It provides risk probability and identifies the most important factors for early prevention. The system is simple, interpretable, and runs in Google Colab. How we built it Used a de-identified heart disease dataset Data cleaning and preprocessing 80/20 train-test split Models: Logistic Regression and Decision Tree Evaluation: Accuracy and Confusion Matrix Feature importance analysis Challenges Understanding medical variables Cleaning missing data Avoiding overfitting Keeping the model interpretable What we learned Machine learning fundamentals Data preprocessing and evaluation Importance of interpretable AI in healthcare Impact & Future Work HeartGuard shows how simple AI can support early risk screening. Future work includes a web risk calculator, larger datasets, and personalized recommendations.

What it does

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What's next for HeartRisk AI: Interpretable CVD Prediction Tool

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• https://colab.research.google.com/drive/your-link