Can I read A data balancing approach towards design of an expert system for Heart Disease Prediction on EtoBox?

A data balancing approach towards design of an expert system for Heart Disease Prediction by Karmakar, Rahul; Ghosh, Udita; Pal, Arpita; Dey, Sattwiki; Malik, Debraj; Sain, Priyabrata is a scholarly article available to read on EtoBox.

What is A data balancing approach towards design of an expert system for Heart Disease Prediction about?

Heart disease is a serious global health issue that claims millions of lives every year. Early detection and precise prediction are critical to the prevention and successful treatment of heart related issues. A lot of research utilizes machine learning (ML) models to forecast cardiac disease and obtain early detection. In order to do predictive analysis on "Heart disease health indicators " dataset. We employed five machine learning methods in this paper: Decision Tree (DT), Random Forest (RF), Linear Discriminant Analysis, Extra Tree Classifier, and AdaBoost. The model is further examined using various feature selection (FS) techniques. To enhance the baseline model, we have separately applied four FS techniques: Sequential Forward FS, Sequential Backward FS, Correlation Matrix, and Chi2. Lastly, K means SMOTE oversampling is applied to the models to enable additional analysis. The findings show that when it came to predicting heart disease, ensemble approaches in particular, random forests performed better than individual classifiers. The presence of smoking, blood pressure, cholesterol, and physical inactivity were among the major predictors that were found. The accuracy of the

Author

Karmakar, Rahul; Ghosh, Udita; Pal, Arpita; Dey, Sattwiki; Malik, Debraj; Sain, Priyabrata

Published

2024

Language

EN