Enhancing Cardiovascular Disease Diagnosis: A Hybrid Model of Whale Optimization Algorithm with Multilayer Deep Perceptive Classifier
Keywords:
Cardiovascular disease, Discretized Binning, Generalized Tversky index similarity, Multilayer Perceptron, Stochastic Bivariate Correlation, Theil-Sen Regression, Whale Optimization Algorithm.Abstract
An efficient Hybridization of Whale Optimized MultiLayer Deep Perceptive Classifier (HWO-MLDPC) is proposed to improve the diagnosis accuracy of cardiovascular disease. The proposed technique includes three main stages: preprocessing, feature selection, and classification. First, the data is preprocessed using the Theil-Sen Regressive Discretized Binning method, which smoothes the raw data into a structured format based on median estimation. After preprocessing, the feature selection process uses stochastic bivariate correlation to identify relevant features based on maximal mutual information. Next, classification with the selected relevant features is performed using the Hybridization of Whale Optimized MultiLayer Deep Perceptive Classifier. The proposed MultiLayer Deep Perceptive Classifier comprises several layers. First, the number of selected features is given to the input layer. Then, the input is transferred to the hidden layer, where feature analysis is performed using the Generalized Tversky index similarity. The sigmoid activation function provides the final disease classification results. At the same time, whale optimization updates the weights of inputs with lesser error to achieve accurate classification results with minimum error at the output layer. Based on the classification results, cardiovascular disease can be diagnosed correctly. Experimental evaluation is carried out using different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of the proposed technique.
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An Y, Huang N, Chen X, Wu F, Wang J. High-risk prediction of cardiovascular diseases via attention-based deep neural networks. IEEE/ACM transactions on computational biology and bioinformatics. 2019 Aug 14; 18 (3):1093-105. doi: https://doi.org/10.1109/TCBB.2019.2935059
Mehmood A, Iqbal M, Mehmood Z, Irtaza A, Nawaz M, Nazir T, Masood M. Prediction of heart disease using deep convolutional neural networks. Arabian Journal for Science and Engineering. 2021 Apr; 46 (4):3409-22. doi: http://dx.doi.org/10.1007/s13369-020-05105-1.
Li P, Hu Y, Liu ZP. Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods. Biomedical Signal Processing and Control. 2021 Apr 1;66:102474. doi: https://doi.org/10.1016/j.bspc.2021.102474
NabaouiaLouridi, Samira Douzi&Bouabid El Ouahidi. Machine learning-based identification of patients with a cardiovascular defect. Journal of Big Data. Springer, Volume 8, 2021, Pages 1-15. doi: https://doi.org/10.1186/s40537-021-00524-9
Samir AA, Rashwan AR, Sallam KM, Chakrabortty RK, Ryan MJ, Abohany AA. Evolutionary algorithm-based convolutional neural network for predicting heart diseases. Computers & Industrial Engineering. 2021 Nov 1;161:107651. doi: https://doi.org/10.1016/j.cie.2021.107651
Irene DS, Sethukarasi T, Vadivelan N. Heart disease prediction using hybrid fuzzy K-medoids attribute weighting method with DBN-KELM based regression model. Medical Hypotheses. 2020 Oct 1;143:110072. doi: https://doi.org/10.1016/j.mehy.2020.110072
Gárate-Escamila AK, El Hassani AH, Andrès E. Classification models for heart disease prediction using feature selection and PCA. Informatics in Medicine Unlocked. 2020 Jan 1;19:100330. doi: https://doi.org/10.1016/j.imu.2020.100330
Zhang D, Chen Y, Chen Y, Ye S, Cai W, Jiang J, Xu Y, Zheng G, Chen M. Heart disease prediction based on the embedded feature selection method and deep neural network. Journal of healthcare engineering. 2021 Sep 29;2021:1-9. doi: https://doi.org/10.1155%2F2021%2F6260022
Rani P, Kumar R, Ahmed NM, Jain A. A decision support system for heart disease prediction based upon machine learning. Journal of Reliable Intelligent Environments. 2021 Sep;7(3):263-75. doi: https://link.springer.com/article/10.1007/s40860-021-00133-6
Ahamed J, Manan Koli A, Ahmad K, Jamal A, Gupta BB. CDPS-IoT: cardiovascular disease prediction system based on IoT using machine learning. doi: http://dx.doi.org/10.9781/ijimai.2021.09.002
Priyanga P, Pattankar VV, Sridevi S. A hybrid recurrent neural network‐logistic chaos‐based whale optimization framework for heart disease prediction with electronic health records. Computational Intelligence. 2021 Feb;37(1):315-43. doi: https://doi.org/10.1111/coin.12405
Martins B, Ferreira D, Neto C, Abelha A, Machado J. Data mining for cardiovascular disease prediction. Journal of medical systems. 2021 Jan;45:1-8. doi: https://doi.org/10.1007/s10916-020-01682-8
Jothi Prakash V, Karthikeyan NK. Enhanced evolutionary feature selection and ensemble method for cardiovascular disease prediction. Interdisciplinary Sciences: Computational Life Sciences. 2021 Sep;13(3):389-412. doi: https://doi.org/10.1007/s12539-021-00430-x
Nawaz MS, Shoaib B, Ashraf MA. Intelligent cardiovascular disease prediction empowered with gradient descent optimization. Heliyon. 2021 May 1;7(5). doi: https://doi.org/10.1016/j.heliyon.2021.e06948
Xie J, Wu R, Wang H, Chen H, Xu X, Kong Y, Zhang W. Prediction of cardiovascular diseases using weight learning based on density information. Neurocomputing. 2021 Sep 10;452:566-75. doi: https://doi.org/10.1016/j.neucom.2020.10.114
Nandy S, Adhikari M, Balasubramanian V, Menon VG, Li X, Zakarya M. An intelligent heart disease prediction system based on swarm-artificial neural network. Neural Computing and Applications. 2023 Jul;35(20):14723-37. doi: https://doi.org/10.1007/s00521-021-06124-1
Dong W, Wan EY, Bedford LE, Wu T, Wong CK, Tang EH, Lam CL. Prediction models for the risk of cardiovascular diseases in Chinese patients with type 2 diabetes mellitus: A systematic review. Public Health. 2020 Sep 1;186:144-56. doi: https://doi.org/10.1016/j.puhe.2020.06.020
Joo G, Song Y, Im H, Park J. Clinical implication of machine learning in predicting the occurrence of cardiovascular disease using big data (Nationwide Cohort Data in Korea). IEEE Access. 2020 Sep 3;8:157643-53. doi: https://doi.org/10.1109/ACCESS.2020.3015757
Guo C, Zhang J, Liu Y, Xie Y, Han Z, Yu J. Recursion enhanced random forest with an improved linear model (RERF-ILM) for heart disease detection on the internet of medical things platform. Ieee Access. 2020 Mar 16;8:59247-56. doi: https://doi.org/10.1109/ACCESS.2020.2981159
Rahim A, Rasheed Y, Azam F, Anwar MW, Rahim MA, Muzaffar AW. An integrated machine learning framework for effective prediction of cardiovascular diseases. IEEE Access. 2021 Jul 20;9:106575-88. doi: https://doi.org/10.1109/ACCESS.2021.3098688
Atimbire, S.A., Appati, J.K. & Owusu, E. Empirical exploration of whale optimization algorithm for heart disease prediction. Sci Rep 14, 4530 (2024). Doi: https://doi.org/10.1038/s41598-024-54990-1
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