https://github.com/alexstratou/predicting-heart-disease-risk-with-scikit-learn

Predicting heart disease risk using combined UCI data and machine learning
https://github.com/alexstratou/predicting-heart-disease-risk-with-scikit-learn

cardiovascular-diseases classification cvd heart heart-disease jupyter-notebook machine-learning python scikit-learn sklearn

Last synced: 16 days ago
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Predicting heart disease risk using combined UCI data and machine learning

• Host: GitHub
• URL: https://github.com/alexstratou/predicting-heart-disease-risk-with-scikit-learn
• Owner: AlexStratou
• License: mit
• Created: 2024-07-28T12:17:32.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-28T14:11:52.000Z (5 months ago)
• Last Synced: 2024-11-10T04:16:16.685Z (about 2 months ago)
• Topics: cardiovascular-diseases, classification, cvd, heart, heart-disease, jupyter-notebook, machine-learning, python, scikit-learn, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 444 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Predicting Heart Disease risk using combined UCI data and machine learning

## Basic info

In this jupyter notebook (Predicting Heart Disease risk using combined UCI data and machine learning.ipynb

), we use scikit-learn to train and test classifiers that can decide if a person is at risk of (or has) heart disease, or not, based on patients' results on simple medical tests.

 

 ## Dataset

The data (heart.csv) was collected from https://www.kaggle.com/fedesoriano/heart-failure-prediction. They are the combination of 5 different datasets that had some overlaping classes. For more info click on the link. The labels are:

1. Age: age of the patient [years]

2. Sex: sex of the patient [M: Male, F: Female]

3. ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]

4. RestingBP: resting blood pressure [mm Hg]

5. Cholesterol: serum cholesterol [mm/dl]

6. FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]

7. RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]

8. MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]

9. ExerciseAngina: exercise-induced angina [Y: Yes, N: No]

10. Oldpeak: oldpeak = ST [Numeric value measured in depression]

11. ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]

12. HeartDisease: output class [1: heart disease, 0: Normal]

The dataset contains 918 (non-duplicate) observations. Notice that the target label is binary, for simplicity.

## Results

After training and testing, we conclude that many of the trained models are succesful in predicting the correct output label. This are some sample confusion matrices of some models:

![image](https://github.com/user-attachments/assets/7677eec4-c8e9-445d-9187-bab1799ca357)

![image](https://github.com/user-attachments/assets/1a4e3812-eabc-46eb-9ab5-5b69da56425a)

## References

1. fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/heart-failure-prediction.