Welcome to the InfoSci Platform
Heart Disease Prediction Model Using Varied Classifiers with Score-Level Fusion

Heart Disease Prediction Model Using Varied Classifiers with Score-Level Fusion

Mohammad Haider Syed
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 39
ISSN: 2643-7937|EISSN: 2643-7945|EISBN13: 9781683183761|DOI: 10.4018/IJSPPC.313587
Cite Article Cite Article

MLA

Syed, Mohammad Haider. "Heart Disease Prediction Model Using Varied Classifiers with Score-Level Fusion." IJSPPC vol.14, no.1 2022: pp.1-39. http://doi.org/10.4018/IJSPPC.313587

APA

Syed, M. H. (2022). Heart Disease Prediction Model Using Varied Classifiers with Score-Level Fusion. International Journal of Security and Privacy in Pervasive Computing (IJSPPC), 14(1), 1-39. http://doi.org/10.4018/IJSPPC.313587

Chicago

Syed, Mohammad Haider. "Heart Disease Prediction Model Using Varied Classifiers with Score-Level Fusion," International Journal of Security and Privacy in Pervasive Computing (IJSPPC) 14, no.1: 1-39. http://doi.org/10.4018/IJSPPC.313587

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

This paper aims to introduce a novel heart disease prediction model. Originally, the input data is subjected for preprocessing, in which the data cleaning takes place. The features like statistical, higher order statistical features, and symmetrical uncertainty are extracted from the preprocessed data. Then, the selected features are subjected to the classification process with an ensemble model that combines the classifiers like deep belief network (DBN), random forest (RF), and neural network (NN). At last, the score level fusion is carried out to provide the final output. To make the classification more precise and accurate, it is intended to tune the weights of DBN more optimally. A new self-adaptive honey bee mating optimization (SAHBMO) algorithm is implemented in this work for this optimal tuning. Finally, the performance of the presented scheme is computed over the existing approaches in terms of different metrics.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.