1. Improved Heart Diseases Risk Prediction Using Optimized Super Learner Ensemble Model.
- Author
-
P, Anuradha and David, Vasantha Kalyani
- Subjects
METAHEURISTIC algorithms ,HEART diseases ,FEATURE selection ,MACHINE learning ,DEATH rate - Abstract
Cardio Vascular Diseases (CVD) has become a serious concern for humans as fatalities rate due to CVD are increasing at an alarming pace. With the aid of machine learning techniques, heart illnesses can be predicted much earlier, and therapy or dietary changes can prevent deaths. By combining predictions from various individual models, the machine learning technique known as ensemble learning improves forecasting accuracy and resiliency. In this work, a Super Learner Ensemble Model is used where the base learners are a diverse combination of linear, probabilistic, bagging, boosting and stacking models. To improve the performance of the Super Learner Ensemble Model, an Optimized Super Learner Ensemble Model (OSLEM) is proposed, where optimal selection of base learners in the ensemble is done based on the pairwise disagreement accuracy diversity measure of classifiers in each best fitness whale obtained by different iterations of Whale Optimization Algorithm (WOA). ModifiedBoostARoota (MBAR), a wrapper feature selection technique is used to choose the most significant features of six different heart datasets and the proposed OSLEM modelled on the selected features exhibits high performance compared to other existing ensemble models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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