5 devlogs
14h 42m 32s
Has been working for predicting of heat related disease
This project uses AI
Used AI to write frontend and readme file
Fixed! Smoking and alcohol now correctly increase risk score.
Root cause was dataset self-reporting bias (only 8.7% smokers) causing
near-zero model weight. Added +5% for smoking, +3% for alcohol on top
of model output. Core model unchanged.
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Shipped this project!
Built CardioSense AI 🫀 — an early cardiovascular disease risk detector trained on 70,000 patient records using RealMLP neural networks (AUC 0.800, +9.6% over baseline).
Stack: Python, PyTorch, pytabkit, SHAP, Streamlit.
Hardest part: getting SHAP to work with RealMLP (not a tree model) and converting the GPU-trained model to run on CPU for cloud deployment. Most proud of: the SHAP explainability showing the model learned real clinical patterns — systolic BP, age, and cholesterol as top predictors, exactly what cardiology literature says.
Built for Hack4Health Byte 2 Beat 2026 🏥
Finished Project
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Recently finished training model.
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Moving into data preprocessing and cleaning process
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Initial setup and data cleaning for the heart disease model. Tracked about 2.5 hours so far
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