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

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.