Project 1

Heart Disease Prediction Using Machine Learning on Clinical Parameters

Project Overview

Heart disease is one of the leading causes of mortality worldwide. Early detection and diagnosis are crucial for effective treatment and improving patient outcomes. This project aims to develop a machine learning model to predict the likelihood of heart disease based on various clinical parameters. The goal is to leverage advanced data analysis techniques to assist healthcare professionals in making informed decisions.

Dataset

The dataset used in this project contains various clinical parameters of patients, including:

1. Age: Age of the patient (in years).
2. Sex: Gender of the patient (1 = male; 0 = female).
3. Chest Pain Type: Type of chest pain experienced by the patient.
   • 0: Typical angina
   • 1: Atypical angina
   • 2: Non-anginal pain
   • 3: Asymptomatic
4. Resting Blood Pressure: Resting blood pressure (in mm Hg on admission).
5. Serum Cholesterol: Serum cholesterol level (in mg/dl).
6. Fasting Blood Sugar: Fasting blood sugar level (> 120 mg/dl) (1 = true; 0 = false).
7. Resting Electrocardiographic Results: Results of resting electrocardiography.
   • 0: Normal
   • 1: ST-T wave abnormality
   • 2: Left ventricular hypertrophy
8. Maximum Heart Rate Achieved: Maximum heart rate during exercise.
9. Exercise Induced Angina: Whether exercise induced angina occurs (1 = yes; 0 = no).
10. Oldpeak: ST depression induced by exercise relative to rest.
11. Slope of Peak Exercise ST Segment: Slope of the peak exercise ST segment.
    • 0: Upsloping
    • 1: Flatsloping
    • 2: Downsloping
12. Number of Major Vessels Colored by Fluoroscopy: Number of major vessels (0-3) colored by fluoroscopy.
13. Thalium Stress Test Result: Results of the thalium stress test.
    • 1,3: Normal
    • 6: Fixed defect
    • 7: Reversible defect
14. Target: Whether the patient has heart disease (1 = yes; 0 = no).

IPYNB File is attached below.