Heart disease detection using inertial Mann relaxed $ CQ $ algorithms for split feasibility problems

Suthep Suantai; Pronpat Peeyada; Andreea Fulga; Watcharaporn Cholamjiak; Suthep Suantai; Pronpat Peeyada; Andreea Fulga; Watcharaporn Cholamjiak

doi:10.3934/math.2023962

AIMS Mathematics

2023, Volume 8, Issue 8: 18898-18918. doi: 10.3934/math.2023962

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Heart disease detection using inertial Mann relaxed $ CQ $ algorithms for split feasibility problems

1.
Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
2.
School of Science, University of Phayao, Phayao 56000, Thailand
3.
Department of Mathematics and Computer Sciences, Universitatea Transilvania Brasov, 500036 Brasov, Romania

Received: 09 January 2023 Revised: 01 May 2023 Accepted: 05 May 2023 Published: 05 June 2023
MSC : 46E20, 46N10, 47H04, 65Z05

This study investigates the weak convergence of the sequences generated by the inertial relaxed $ CQ $ algorithm with Mann's iteration for solving the split feasibility problem in real Hilbert spaces. Moreover, we present the advantage of our algorithm by choosing a wider range of parameters than the recent methods. Finally, we apply our algorithm to solve the classification problem using the heart disease dataset collected from the UCI machine learning repository as a training set. The result shows that our algorithm performs better than many machine learning methods and also extreme learning machine with fast iterative shrinkage-thresholding algorithm (FISTA) and inertial relaxed $ CQ $ algorithm (IRCQA) under consideration according to accuracy, precision, recall, and F1-score.
- weak convergence,
- inertial technique,
- split feasibility problem,
- data classification,
- heart disease data
Citation: Suthep Suantai, Pronpat Peeyada, Andreea Fulga, Watcharaporn Cholamjiak. Heart disease detection using inertial Mann relaxed $ CQ $ algorithms for split feasibility problems[J]. AIMS Mathematics, 2023, 8(8): 18898-18918. doi: 10.3934/math.2023962

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Abstract

This study investigates the weak convergence of the sequences generated by the inertial relaxed $ CQ $ algorithm with Mann's iteration for solving the split feasibility problem in real Hilbert spaces. Moreover, we present the advantage of our algorithm by choosing a wider range of parameters than the recent methods. Finally, we apply our algorithm to solve the classification problem using the heart disease dataset collected from the UCI machine learning repository as a training set. The result shows that our algorithm performs better than many machine learning methods and also extreme learning machine with fast iterative shrinkage-thresholding algorithm (FISTA) and inertial relaxed $ CQ $ algorithm (IRCQA) under consideration according to accuracy, precision, recall, and F1-score.

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