Heart Failure Prediction using Machine Learning with Exploratory Data Analysis
Harsh Agrawal, Janki Chandiwala, Sarvesh Agrawal, Yash Goyal
Abstract
According to WHO, cardiovascular diseases are the number 1 cause of death globally. It causes the death of more than 12 million people every year worldwide. The main issue that needs to be resolved is that one should be warned well before time to take precautionary measures. Thus, in this paper, we propose a radical solution based on ensemble learning combining 10 different classification algorithms namely AdaBoost, CatBoost, Decision Trees, KNN, Logistic regression, Light GBM, Gaussian Naïve Bayes, Random Forest, SVM and XGBoost. This ensemble model was able to achieve a test accuracy of 85.2% and test recall of 87.50%. We used the data collected from the Framingham Heart study which includes 15 attributes and 4200+ records. Moreover, we performed extensive Exploratory Data Analysis to understand the importance of each attribute in causing heart failure.