@Article{csse.2023.035244,
AUTHOR = {Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Majid Bashir Malik, Anand Nayyar, Kyung Sup Kwak},
TITLE = {An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {46},
YEAR = {2023},
NUMBER = {3},
PAGES = {3993--4006},
URL = {http://www.techscience.com/csse/v46n3/52227},
ISSN = {},
ABSTRACT = {Cardiovascular disease is among the top five fatal diseases that affect
lives worldwide. Therefore, its early prediction and detection are crucial, allowing
one to take proper and necessary measures at earlier stages. Machine learning
(ML) techniques are used to assist healthcare providers in better diagnosing heart
disease. This study employed three boosting algorithms, namely, gradient boost,
XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML
repository. Exploratory data analysis is performed to find the characteristics of
data samples about descriptive and inferential statistics. Specifically, it was carried
out to identify and replace outliers using the interquartile range and detect and
replace the missing values using the imputation method. Results were recorded
before and after the data preprocessing techniques were applied. Out of all the
algorithms, gradient boosting achieved the highest accuracy rate of 92.20% for
the proposed model. The proposed model yielded better results with gradient
boosting in terms of precision, recall, and f1-score. It attained better prediction
performance than the existing works and can be used for other diseases that share
common features using transfer learning.},
DOI = {10.32604/csse.2023.035244}
}