Heart Disease Predictive Model Using Filter-Based Selection Techniques and Tree-Like Classifiers

Awe Oluwayomi, Aiyeniko Olukayode, Adedokun Olufemi Adewale, Funso Bukola Omolara and Samuel Ruth Medinat
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Awe Oluwayomi: Department of Computer Science, University of Lagos, Lagos State, Nigeria.
Aiyeniko Olukayode: Department of Computer Science, Lagos State University, Lagos State, Nigeria
Adedokun Olufemi Adewale: Department of Computer Science, University of Ilorin, Ilorin, Nigeria
Funso Bukola Omolara: Department of Computer Science Kogi State Polytechnic, Kogi State, Nigeria
Samuel Ruth Medinat: Department of Computer Science Kogi State Polytechnic, Kogi State, Nigeria

International Journal of Research and Innovation in Applied Science, 2022, vol. 7, issue 8, 07-11

Abstract: The attribute selection is considered a major phase that eliminates redundant attributes thereby improving the accuracy of the predictive or diagnostic model. Designing a model with unrelated attributes may influence the accuracy or result in more memory space used during diagnosis or prediction. This paper examined the impact of the filter-based attribute selection technique on the heart disease diagnostic model. Three filter-based techniques: Relief-F, Information Gain and Chi-square were applied to the heart disease dataset. Five tree-like learning algorithms: ID3 (Iterative Dichotomiser 3), C4.5 Decision Tree, Reptree (RP), Random Forest (RF), Classification and Regression Tree (CART) were applied to classify the reduced attributes. The experimental results in terms of accuracy, precision and recall showed that the relief-f attribute selection outperformed information gain and chi-square with the best predictive accuracy of 93.4983% in IDE, the precision value of 0.93500 in IDE and recall value of 0.93500 in IDE classifier.

Date: 2022
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