An inertial projective forward-backward-forward algorithm for constrained convex minimization problems and application 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.