A binary classifier build using PyTorch using the Kaggle dataset Cleveland Heart disease.
The dataset consists of 303 individuals data. There are 14 columns in the dataset, which are described below.
- Age: displays the age of the individual.
- Sex: displays the gender of the individual using the following format : 1 = male 0 = female
- Chest-pain type: displays the type of chest-pain experienced by the individual using the following format : 1 = typical angina 2 = atypical angina 3 = non — anginal pain 4 = asymptotic
- Resting Blood Pressure: displays the resting blood pressure value of an individual in mmHg (unit)
- Serum Cholestrol: displays the serum cholesterol in mg/dl (unit)
- Fasting Blood Sugar: compares the fasting blood sugar value of an individual with 120mg/dl. If fasting blood sugar > 120mg/dl then : 1 (true) else : 0 (false)
- Resting ECG : displays resting electrocardiographic results 0 = normal 1 = having ST-T wave abnormality 2 = left ventricular hyperthrophy
- Max heart rate achieved : displays the max heart rate achieved by an individual.
- Exercise induced angina : 1 = yes 0 = no
- ST depression induced by exercise relative to rest: displays the value which is an integer or float.
- Peak exercise ST segment : 1 = upsloping 2 = flat 3 = downsloping
- Number of major vessels (0–3) colored by flourosopy : displays the value as integer or float.
- Thal : displays the thalassemia : 3 = normal 6 = fixed defect 7 = reversible defect
- Diagnosis of heart disease : Displays whether the individual is suffering from heart disease or not : 0 = absence 1, 2, 3, 4 = present.
Here, target = 1 implies that the person is suffering from heart disease and target = 0 implies the person is not suffering.
