Heart Disease Prediction Using Hybrid Algorithm

System Architecture – Heart Disease Prediction

IMPLEMENTATION METHODOLOGY

The proposed work is implemented in Python 3.6.4 with libraries scikit-learn, pandas, matplotlib and other mandatory libraries. We downloaded dataset from uci.edu. The data downloaded contains binary classes of heart disease. Machine learning algorithm is applied such as decision tree and random forest along with hybrid model.

DATA DICTIONARY

The dataset collected with attributes age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slop, ca, thal, pred_attribute.

Modules

The modules included in our implementation are as follows

  • Decision Tree
  • Random forest
  • Hybrid RF & Linear model

Python – Demo

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