Predicting heart disease using Machine Learning

A machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes.

• Problem definition
• Data
• Evaluation
• Features
• Modelling
• Experimentation

1. Problem Definition

In a statement,

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

2. Data

Data obtained from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/Heart+Disease

3. Evaluation

If we can reach 95% accuracy in predicting whether or not a patient has heart disease during the proof of concept, we will pursue the project

4. Features

Create data dictionary

• age- age in years
• sex- (1 = male; 0 = female)
• cp- chest pain type
• 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
• 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
• ca- number of major vessels (0-3) colored by flourosopy
• thal- 3 = normal; 6 = fixed defect; 7 = reversable defect
• target- 1 or 0

Preparing the tools

The following tools are going to be used for data analysis, manipulation and modelling:

• pandas
• matplotlib
• numpy
• scikit-learn

5. Results

Feature importance

Correlation matrix

ROC curve

Please leave a ⭐ if you find this useful! Feel free to reach out to me for any queries!

Happy Coding!