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. Check dataset on Kaggle or UCI.edu.
Given clinical parameters about a patient, can we predict whether or not they have heart disease?
Explanation of fields in dataset
age- age in yearssex- (1 = male; 0 = female)cp- chest pain type- 0: Typical angina
- 1: Atypical angina
- 2: Non-anginal pain
- 3: Asymptomatic
trestbps- resting blood pressure (in mm Hg on admission to the hospital)chol- Serum cholesterole in mg/dlfbs- (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)restecg- resting electrocardiographic results- 0: Nothing to note
- 1: ST-T Wave abnormality
- 2: Possible or definite left ventricular hypertrophy
thalach- maximum heart rate achievedexang- exercise induced angina (1 = yes; 0 = no)oldpeak- ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress moreslope- 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
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)
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
target- have disease or not (1=yes, 0=no) (= the predicted attribute)