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

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.