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heart-disease-case-study
skdcbjas/heart-disease-case-study
heart-disease-case-study is a machine learning model from skdcbjas. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for keras.
This repository contains a collection of 5 trained models comparing traditional Machine Learning algorithms against Deep Learning variants on an imbalanced tabular healthcare dataset.
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At a glance
- Library
- keras
- Access
- Public
- Created
- Jul 13, 2026
- Updated
- Jul 13, 2026
- SHA
- 4f8a0a4c
Model card
From the Hugging Face model README
10-Year Heart Disease Risk (Framingham) Case Study
This repository contains a collection of 5 trained models comparing traditional Machine Learning algorithms against Deep Learning variants on an imbalanced tabular healthcare dataset.
Dataset Profile
- Task: Binary Classification (
TenYearCHD) - Rows: 4,238
- Features: 15 clinical/demographic columns
Performance Evaluation Summary
The models were tracked across Accuracy and F1-Score:
- Logistic Regression (Baseline): Accuracy 0.718 | F1-Score 0.055
- Random Forest (Baseline): Accuracy 0.703 | F1-Score 0.067
- Random Forest (Tuned): Accuracy 0.700 | F1-Score 0.073
- MLP/ANN (Baseline): Accuracy 0.683 | F1-Score 0.220
- MLP/ANN (Tuned): Accuracy 0.705 | F1-Score 0.023
Technical Takeaway
Due to class imbalance (~15% positive cases), standard accuracy measures are deceptive. Baseline MLP achieved the highest F1-Score optimization prior to hyperparameter tightening.
- Library
- keras
- Created
- Jul 13, 2026
- Updated
- Jul 13, 2026