| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|---|---|---|---|---|---|---|---|
| v1.0.0-preview | Dynamic API | Up-to-date | Console app | .txt files | Heart disease classification | Binary classification | FastTree |
In this introductory sample, you'll see how to use ML.NET to predict type of heart disease. In the world of machine learning, this type of prediction is known as binary classification.
##Dataset The dataset used is this: [UCI Heart disease] (https://archive.ics.uci.edu/ml/datasets/heart+Disease) This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. For this dataset thanks to :
- Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
- University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
- University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
- V.A. Medical Center, Long Beach and Cleveland Clinic Foundation:Robert Detrano, M.D., Ph.D.
This problem is centered around predicting the presence of hearth disease based on 14 attributes. To solve this problem, we will build an ML model that takes as inputs 4 parameters: Attribute Information:
- (age) - Age
- (sex) - (1 = male; 0 = female)
- (cp) chest pain type -- Value 1: typical angina -- Value 2: atypical angina -- Value 3: non-anginal pain -- Value 4: asymptomatic
- (trestbps) - resting blood pressure (in mm Hg on admission to the hospital)
- (chol) - serum cholestoral in mg/dl
- (fbs) - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
- (restecg) - esting electrocardiographic results -- Value 0: normal -- Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) -- Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
- (thalach) - maximum heart rate achieved
- (exang) - exercise induced angina (1 = yes; 0 = no)
- (oldpeak) - ST depression induced by exercise relative to rest
- (slope) - the slope of the peak exercise ST segment -- Value 1: upsloping -- Value 2: flat -- Value 3: downsloping
- (ca) - number of major vessels (0-3) colored by flourosopy
- (thal) - 3 = normal; 6 = fixed defect; 7 = reversable defect
- (num) (the predicted attribute) diagnosis of heart disease (angiographic disease status) -- Value 0: < 50% diameter narrowing -- Value 1: > 50% diameter narrowing (in any major vessel: attributes 59 through 68 are vessels)
and predicts the presence of heart disease in the patient with integer values from 0 to 4: Experiments with the Cleveland database (dataset used for this example) have concentrated on simply attempting to distinguish presence (value 1) from absence (value 0).
The generalized problem of binary classification is to classify items into items into one of the two classes.