An inertial projective forward-backward-forward algorithm for constrained convex minimization problems and application to cardiovascular disease prediction
Year: 2024
Type: article
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.
Authors: P. Cholamjiak, W. Cholamjiak, K. Kankam
Institutions: University of Phayao, Suan Dusit University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4403855873