https://github.com/alakise/heart-disease

This study is the source code of the project named "Building a Supportive Artificial Neural Network Model in the Diagnosis of Heart Disease" presented at the 2021 Marmara University Student Congress. Check dataset on Kaggle or UCI.edu.
https://github.com/alakise/heart-disease

heart keras tensorflow

Last synced: 11 days ago
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This study is the source code of the project named "Building a Supportive Artificial Neural Network Model in the Diagnosis of Heart Disease" presented at the 2021 Marmara University Student Congress. Check dataset on Kaggle or UCI.edu.

• Host: GitHub
• URL: https://github.com/alakise/heart-disease
• Owner: alakise
• License: mit
• Created: 2021-03-08T11:51:00.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-06-01T20:31:32.000Z (over 3 years ago)
• Last Synced: 2024-11-08T06:51:40.332Z (2 months ago)
• Topics: heart, keras, tensorflow
• Language: Jupyter Notebook
• Homepage: https://www.marmarastudentcongress.com/
• Size: 2.29 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Building a Supportive Artificial Neural Network Model in the Diagnosis of Heart Disease | Kalp Hastalığı Tanısında Destekleyici Yapay Nöral Ağ Modeli Oluşturulması

This study is the source code of the project named "Building a Supportive Artificial Neural Network Model in the Diagnosis of Heart Disease" presented at the [2021 Marmara University Student Congress](https://www.marmarastudentcongress.com/). Check dataset on [Kaggle](https://www.kaggle.com/nareshbhat/health-care-data-set-on-heart-attack-possibility) or [UCI.edu](https://archive.ics.uci.edu/ml/datasets/heart+disease). 

## Problem Definition

Given clinical parameters about a patient, can we predict whether or not they have heart disease?

## Features

Explanation of fields in dataset

### Data Dictionary

1. `age` - age in years

2. `sex` - (1 = male; 0 = female)

3. `cp` - chest pain type

    * 0: Typical angina

    * 1: Atypical angina

    * 2: Non-anginal pain

    * 3: Asymptomatic

4. `trestbps` - resting blood pressure (in mm Hg on admission to the hospital) 

5. `chol` - Serum cholesterole in mg/dl

6. `fbs` - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)

7. `restecg` - resting electrocardiographic results

    * 0: Nothing to note

    * 1: ST-T Wave abnormality

    * 2: Possible or definite left ventricular hypertrophy

8. `thalach` - maximum heart rate achieved

9. `exang` - exercise induced angina (1 = yes; 0 = no)

10. `oldpeak` - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more

11. `slope` - the slope of the peak exercise ST segment

    * 0: Upsloping: better heart rate with excercise (uncommon)

    * 1: Flatsloping: minimal change (typical healthy heart)

    * 2: Downslopins: signs of unhealthy heart

12. `ca` - number of major vessels (0-3) colored by flourosopy

    * colored vessel means the doctor can see the blood passing through

    * the more blood movement the better (no clots)

13. `thal` - thalium stress result

    * 1,3: normal

    * 6: fixed defect: used to be defect but ok now

    * 7: reversable defect: no proper blood movement when excercising

14. `target` - have disease or not (1=yes, 0=no) (= the predicted attribute)