README.md

Predict heart disease using machine learning

Define Problem Statement

Given clinical attributes of a patient, predict whether they have heart disease or not?

Accuracy Achieved: 88 %

Algorithms

1. KNN
2. Logistic Regression
3. Random Forest

Features

Create data dictionary

• age: The person’s age in years
• sex: The person’s sex (1 = male, 0 = female)
• cp: Chest pain type
  • Value 0: asymptomatic
  • Value 1: atypical angina
  • Value 2: non-anginal pain
  • Value 3: typical angina
• trestbps: The person’s resting blood pressure (mm Hg on admission to the hospital)
• chol: The person’s cholesterol measurement in mg/dl
• fbs: The person’s fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false)
• restecg: Resting electrocardiographic results
  • Value 0: showing probable or definite left ventricular hypertrophy by Estes’ criteria
  • Value 1: normal
  • Value 2: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
• thalach: The person’s 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 0: downsloping
  • Value 1: flat
  • Value 2: upsloping
• ca: The number of major vessels (0–3)
• thal: A blood disorder called thalassemia
  • Value 0: NULL (dropped from the dataset previously)
  • Value 1: fixed defect (no blood flow in some part of the heart)
  • Value 2: normal blood flow
  • Value 3: reversible defect (a blood flow is observed but it is not normal)
• target: Heart disease (1 = no, 0 = yes)

To run this app locally:

1. Run npm run dev in client dir.
2. Run npm start in node_server dir.
3. Run python app.py in python_server dir.