A12176 Smart phone based “heart attack” risk prediction; innovation of clinical and social approach for preventive cardiac health

Detection of Coronary Artery Using Novel Optimized Grid Search-based MLP

“…Again, our heart disease detector outperformed the existing approaches in terms of accuracy. We observed a substantial increase in the accurate detection by comparing our method with existing approaches [29][30][31][32][33][34][35][36][37][38][39][40]. The above experimental outcomes by comparing the performance of our method with machine learning approaches and the existing approaches clearly depict that the proposed GridSearch-based MLP has shown significant improvements in the accurate detection of the disease.…”

“…We conducted this experiment to compare the performance of the proposed system against the existing state-of-the-art methods. For this purpose, we took the experimental results directly from the existing state-of-the-art papers [29][30][31][32][33][34][35][36][37][38][39][40] without implementing the methods as shown in Table II. The detailed results of the methods [29][30][31][32][33][34][35][36][37][38][39][40] are given in Table II.…”

“…For this purpose, we took the experimental results directly from the existing state-of-the-art papers [29][30][31][32][33][34][35][36][37][38][39][40] without implementing the methods as shown in Table II. The detailed results of the methods [29][30][31][32][33][34][35][36][37][38][39][40] are given in Table II. From the results reported in Table II, we observed that Sahan et al [34] performs worst and achieved an accuracy of 82.59%.…”

“…Abdar, M., et al [36] employed N2Genetic-NuSVM and achieved an accuracy of 93.08% while the proposed method performs best and achieved an accuracy of 95.12%. Experimental results and comparative analysis of the proposed method against the existing state-of-the-art methods [29][30][31][32][33][34][35][36][37][38][39][40] illustrate that our method has superior performance to other methods. More specifically, we observe that our method achieved 9.88%, 11.75%, 11.17%, 8.32%, 6.24%, 12.53%, 9.52%, and 2.04% higher accuracy than the existing state-of-the-art methods [29][30][31][32][33][34][35][36].…”

“…Author Method Accuracy% Helmy, et al [29] Algebraic sigmoid 85.24 Wang, et al [30] SVM 83.37 Özşen, et al [31] Hybrid similarity measure 83.95 Kahramanli, et al [32] Hybrid neural network 86.8 Yan. et al [33] ICA-SVM 88.88 Sahan et al [34] AWAIS 82.59 Duch et al [35] KNN 85.6 Abdar, M., et al [36] N2Genetic-NuSVM 93.08 Firdaus, F. F., et al [37] DNN-Bayesian Optimization 91.67 Qrenawi, Mohammed et al [38] Rmonto ontology-driven data mining approach 90.0 Shahid Mehmood et al [39] ANN-based attribute extraction technique 94.7 Kaanchan More et al [40] Risk factor-based approach 86.7 Proposed Grid Search-based MLP 95.12 DISCUSSION.…”