An Ensemble Learning based Web Application to predict the risk of Heart Disease
Main Article Content
Abstract
With an approximate 17.9 million annual victims, heart disease stands out as a prominent cause of deaths worldwide, whose fatality can be reduced to a great extent with an expeditious diagnosis. Herein we propose an ensemble learning-based heart disease prediction system. The UCI Heart Disease dataset has been utilized in this work. Relevant data mining methodology has been adopted to create six predictive models. Appropriate hyperparameters were optimized with the help of GridSearchCV along with 5-fold cross-validation. Recall value and ROC score were the performance metrics considered relevant to judge the performance of the models. The best performing models were picked to create a heterogeneous ensemble. The proposed ensemble produced a ROC score of 0.84 and a recall value of 0.94. The suggested ensemble has been found to enhance the predictive capabilities of the classic algorithms
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.