Nissa, N.; Jamwal, S.; Neshat, M. A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques. Computation2024, 12, 15.
Nissa, N.; Jamwal, S.; Neshat, M. A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques. Computation 2024, 12, 15.
Nissa, N.; Jamwal, S.; Neshat, M. A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques. Computation2024, 12, 15.
Nissa, N.; Jamwal, S.; Neshat, M. A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques. Computation 2024, 12, 15.
Abstract
The World Health Organization (WHO) has released reports indicating that heart disorders hold the unfortunate distinction of being the primary cause of death worldwide. Shockingly, an astonishing estimated 17.9 million lives are claimed by heart diseases annually, accounting for an alarming 31% of all global deaths. With all the flaws in the clinical situation, it is frequently challenging to assess the severity of cardiac disease and forecast its course of progression due to heterogeneity and complex interplay of factors. Therefore, early heart disease detection is essential for effective therapy. To address these challenges, Machine Learning (ML) boosting algorithms play a pivotal role as the main components of predictive analytics required to do this. The main objective of this study is to develop a comprehensive comparative framework to predict heart diseases using state-of-the-art machine learning with boosting techniques such as Decision Tree, Random Forest, Gradient Boosting, Catboost, XGboost, Light GBM, and Adaboost. To evaluate the performance of the models, a large heart disorders dataset is used from the UCI machine learning repository, comprised of 26 feature-based numerical and categorical attributes with 8763 samples from all over the globe. The Experimental results reveal that AdaBoost attained the highest accuracy of 95% and outperforms other algorithms concerning various performance measures like precision= 0.98, recall= 0.95, specificity= 0.95, f1-score=0.01, Negative predicted value= 0.83, False positive rate= 0.04, False negative rate= 0.04 and False Development rate= 0.01.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
