Heart_Disease_Prediction

This is a Heart Disease Data Set, collected from the UCI Machine Learning Repository. The complete collection consists of four individual databases collected from four different institutions located in Cleveland, Hungary, Switzerland, and the VA Long Beach. Each database has the same instance format. While the databases have 76 raw attributes, only 14 of them are actually used. Here are the descriptions of the columns for the dataset:

Features:

age: age in years

sex: sex (1 = male; 0 = female)

cp: chest pain type

• 1 = typical angina
• 2 = atypical angina
• 3 = non-anginal pain
• 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: resting electrocardiographic results

• 0 = normal
• 1 = having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
• 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

• 1 = upsloping
• 2 = flat
• 3 = downsloping ca: number of major vessels (0-3) colored by flourosopy

thal: 3 = normal; 6 = fixed defect; 7 = reversable defect

Target variable:

hd: heart disease

Objectives:

Our goal is to apply machine learning algorithms. On this dataset, we used a Decision Tree algorithm to find out whether a patient has heart disease or not.

Reference of this dataset: https://archive.ics.uci.edu/ml/datasets/heart+disease