d-behl / Heart-Disease-Prediction

Predict heart disease (aniographic vessel diameter narrowing) using Machine Learning algorithms

Heart Disease Prediction

The aim of this project is to to predict heart disease (angiographic disease status) from a dataset of 14 features.

Initially, EDA is performed on the dataset to figure out the important features in the given data. Then, 4 different machine learning algorithms (Logistic Regression, SVM, Random Forest, XGBoost) to predict Heart Disease are compared and the best model among them is chosen.

Dataset Used

UCI Heart Disease Data Set (https://archive.ics.uci.edu/ml/datasets/Heart+Disease)

Results

• The best results measured by AUC and Accuracy are obtained from a Logistic Regression model (AUC 0.96, Accuracy 0.88), followed by a Gradient Boosting model.
• From the set of 14 feaures, the most important ones to predict heart failure are whether or not there is a reversable defect in Thalassemia followed by whether or not there is an occurrence of asymptomatic chest pain.