Ensemble Based Hybrid Variable Selection Method for Heart Disease Classification
Main Article Content
Abstract
In this paper, we proposed an ensemble-based hybrid variable selection model that aggregates various variable selection methods results based on majority voting approach to select a risk features subset in the heart disease datasets. The performance of the devised framework is evaluated using Z-Alizadeh Sani heart disease dataset from the UCI repository. Besides, we also compare this devised method with three non-ensemble variable selection methods namely the Chi-square test, Recursive Feature Elimination, and L1-Regularization. The selection process of the devised method is validated through a random forest classifier, it performs better in terms of specificity, sensitivity, accuracy, precision, and f1-score. The proposed method significantly enhances the accuracy of the heart disease classification model.
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.