Predicting Heart Disease using Machine Learning

This repository consist of ML experiments in form of notebook, which looks into using various Python-libraries of Machine Learning and Data Science in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes.

Prerequisites

Environment

Install MiniConda, for detailed setup check

Libraries

• Pandas
• NumPy
• Matplotlib
• Seaborn (for heatmaps)
• Scikit-Learn

Workspace

• Jupyter Notebook

Steps followed

1. Problem Definition
2. Data Exploration
3. Evaluation
4. Features
5. Modelling
6. Experimentation

Problem Definition

In a statement,

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

Data Source

The original data came from the Cleavland data from the UCI Machine Learning Repository.

Download it from UCI Heart Disease Data Set or Kaggle

Data Format

Data Features (Dictionary)

1. age: age in years
2. sex: sex (1 = male; 0 = female)
3. cp: chest pain type
   • Value 0: typical angina
   • Value 1: atypical angina
   • Value 2: non-anginal pain
   • Value 3: asymptomatic
4. trestbps: resting blood pressure (in mm Hg on admission to the hospital)
5. chol: serum cholestoral in mg/dl
6. fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
7. restecg: resting 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
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
11. slope: the slope of the peak exercise ST segment
    • Value 0: upsloping
    • Value 1: flat
    • Value 2: downsloping
12. ca: number of major vessels (0-3) colored by flourosopy
13. thal: 0 = normal; 1 = fixed defect; 2 = reversable defect
14. target: 0 = no disease, 1 = disease

Models Used

1. Logistic Regression
2. RandomForest Classifier
3. K-Nearest Neighbours