An inertial projective forward-backward-forward algorithm for constrained convex minimization problems and application to cardiovascular disease prediction

Volume 37, Issue 3, pp 347--360 https://dx.doi.org/10.22436/jmcs.037.03.08

Publication Date: October 25, 2024 Submission Date: June 26, 2024 Revision Date: August 15, 2024 Accteptance Date: September 11, 2024

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Authors

P. Cholamjiak - School of Science, ‎University of Phayao, ‎Phayao 56000, ‎Thailand. W. Cholamjiak - School of Science, ‎University of Phayao, ‎Phayao 56000, ‎Thailand. K. Kankam - Elementary Education Program‎, ‎Faculty of Education, ‎Suan Dusit University Lampang Center, ‎Lampang 52100, ‎Thailand.

Abstract

‎In this paper‎, ‎we introduce a novel machine learning algorithm designed for the classification of cardiovascular diseases‎. ‎The proposed inertial projected forward-backward-forward algorithm is developed to address constrained minimization in Hilbert spaces‎, ‎with a specific focus on improving the accuracy of disease classification‎. ‎Utilizing inertial techniques‎, ‎the algorithm employs a projected forward-backward-forward strategy‎, ‎demonstrating convergence under mild conditions‎. ‎Evaluation of the algorithm employs four essential performance metrics-accuracy‎, ‎F1-score‎, ‎recall‎, ‎and precision to gauge its effectiveness compared to alternative classification models‎. ‎Results indicate significant performance gains‎, ‎achieving peak metrics of 77.50\% accuracy‎, ‎71.57\% precision‎, ‎91.27\% recall‎, ‎and 80.23\% F1-score‎, ‎thereby surpassing established benchmarks in machine learning models for cardiovascular disease classification‎.

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Keywords

• Projection method
• inertial technique
• classification problem
• constrained minimization problem

MSC

•  65K05
•  90C25
•  90C30

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