Ensemble classification technique for heart disease prediction with meta-heuristic-enabled training system

• Autorzy: Kumar Parvathaneni Rajendra , Ravichandran Suban , Narayana Satyala

Identyfikatory

DOI

10.1515/bams-2020-0033

• Języki publikacji: EN

Abstrakty

EN

Objectives: This research work exclusively aims to develop a novel heart disease prediction framework including three major phases, namely proposed feature extraction, dimensionality reduction, and proposed ensemble-based classification. Methods: As the novelty, the training of NN is carried out by a new enhanced optimization algorithm referred to as Sea Lion with CanberraDistance (S-CDF) via tuning the optimal weights. The improved S-CDF algorithm is the extended version of the existing “Sea Lion Optimization (SLnO)”. Initially, the statistical and higher-order statistical features are extracted including central tendency, degree of dispersion, and qualitative variation, respectively. However, in this scenario, the “curse of dimensionality” seems to be the greatest issue, such that there is a necessity of dimensionality reduction in the extracted features. Hence, the principal component analysis (PCA)- based feature reduction approachis deployed here. Finally, the dimensional concentrated features are fed as the input to the proposed ensemble technique with “Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN)” with optimized Neural Network (NN) as the final classifier. Results: An elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques. Conclusions: From the experiment outcomes, it is proved that the accuracy of the proposed work with the proposed feature set is 5, 42.85, and 10% superior to the performance with other feature sets like central tendency + dispersion feature, central tendency qualitative variation, and dispersion qualitative variation, respectively. Results: Finally, the comparative evaluation shows that the presented work is appropriate for heart disease prediction as it has high accuracy than the traditional works.

Słowa kluczowe

EN

central tendency; degree of dispersion; ensemble classification; heart disease prediction; optimization

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2021
• Tom: Vol. 17, no. 2
• Strony: 119--136
• Opis fizyczny: Bibliogr. 43 poz., rys., tab.

Twórcy

autor

Kumar Parvathaneni Rajendra

• Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, 608002 Chidambaram, Tamil Nadu, India

autor

Ravichandran Suban

• Department of Information Technology, Faculty of Engineering and Technology, Annamalai University Annamalainagar - 608002, Tamil Nadu, India

autor

Narayana Satyala

• Gudlavalleru Engineering College Gudlavalleru- 521356, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).