Ha J, Kambe M, Pe J. Data mining: concepts and techniques. (2011), 1–703 https://doi.org/10.1016/C2009-0-61819-5.

Cai J, Luo J, Wang S, Yang S. Feature selection in machine learning: a new perspective. Neurocomputing. 2018;300:70–9. https://doi.org/10.1016/J.NEUCOM.2017.11.077.

Article  MATH  Google Scholar 

Di Angelantonio E, Gao P, Pennells L, Kaptoge S, et al. Lipid-related markers and cardiovascular disease prediction. JAMA. 2012;307(23):2499–506. https://doi.org/10.1001/JAMA.2012.6571.

Article  Google Scholar 

Tang J, Wang Y, Luo Y, Jianbo F, et al. Computational advances of tumor marker selection and sample classification in cancer proteomics. Comput Struct Biotechnol J. 2020;18:2012. https://doi.org/10.1016/j.csbj.2020.07.009.

Article  MATH  Google Scholar 

Tang J, Wang Y, Jianbo Fu, Zhou Y, et al. A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies. Brief Bioinform. 2020;21(4):1378–90. https://doi.org/10.1093/BIB/BBZ061.

Article  MATH  Google Scholar 

Santo K, Redfern J. Digital health innovations to improve cardiovascular disease care. Curr Atheroscler Rep. 2020;22:1–10. https://doi.org/10.1007/s11883-020-00889-x.

Article  MATH  Google Scholar 

Zou Q, Kaiyang Qu, Luo Y, Yin D, et al. Predicting diabetes mellitus with machine learning techniques. Front Genet. 2018;9: 416440. https://doi.org/10.3389/FGENE.2018.00515/BIBTEX.

Article  Google Scholar 

Feng Q, Chen L, Philip Chen CL. Optimize real-valued RBM with bidirectional autoencoder. iFUZZY 2015 - 2015 Int Conf Fuzzy Theory and Its App, Conference Digest. 2016;1:22–7. https://doi.org/10.1109/IFUZZY.2015.7391888.

Article  MATH  Google Scholar 

Kim JK, Han YS, Lee JS. Particle swarm optimization–deep belief network–based rare class prediction model for highly class imbalance problem. Concurr Comput: Practice Exper. 2017;29(11):e4128. https://doi.org/10.1002/CPE.4128.

Article  MATH  Google Scholar 

Houssein EH, Hammad A, Emam MM, Ali AA. An enhanced coati optimization algorithm for global optimization and feature selection in EEG emotion recognition. Comput Biol Med. 2024;173: 108329. https://doi.org/10.1016/J.COMPBIOMED.2024.108329.

Article  Google Scholar 

Yaqoob A, Verma NK, Aziz RM, Saxena A. Enhancing feature selection through metaheuristic hybrid cuckoo search and Harris hawks optimization for cancer classification. Metaheuristics Machine Learning (2024) pp 95–134. https://doi.org/10.1002/9781394233953.CH4

Di Angelantonio E, Chowdhury R, Sarwar N, Ray KK, et al. B-type natriuretic peptides and cardiovascular risk: systematic review and meta-analysis of 40 prospective studies. Circulation. 2009;120(22):2177–87. https://doi.org/10.1161/CIRCULATIONAHA.109.884866.

Article  Google Scholar 

Zeleznik R, Foldyna B, Eslami P, … Weiss J, Nature et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nature. comR Zeleznik, B Foldyna, P Eslami, J Weiss, I Alexander, J Taron, C Parmar, RM Alvi, D Banerji Nature communications, 2021•nature.com, Accessed: Mar. 25, 2024. [Online]. Available: https://www.nature.com/articles/s41467-021-20966-2

Singh J, Kaur R. - Indian J Sci Technol, and undefined 2016, Cardio vascular disease classification ensemble optimization using genetic algorithm and neural network. sciresol.s3.us-east-2. Amazonaws J Singh, R Kaur Indian J Sci Technol, 2016•sciresol.s3.us-east-2. amazonaws …, https://doi.org/10.17485/ijst/2016/v9i(S1)/98900.

Stewart J, Manmathan G, Wilkinson P. Primary prevention of cardiovascular disease: a review of contemporary guidance and literature. JRSM Cardiovasc Dis. 2017;6:204800401668721. https://doi.org/10.1177/2048004016687211.

Article  Google Scholar 

Lin X, Zhu F, Lam TH, Jiang CQ, et al. Egg consumption and the risk of cardiovascular disease and all-cause mortality: Guangzhou Biobank Cohort Study and meta-analyses. Eur J Nutr. 2019;58(2):785–96. https://doi.org/10.1007/S00394-018-1692-3/METRICS.

Article  MATH  Google Scholar 

Bolón-Canedo V, Remeseiro B. Feature selection in image analysis: a survey. Artif Intell Rev. 2020;53(4):2905–31. https://doi.org/10.1007/S10462-019-09750-3/FIGURES/8.

Article  MATH  Google Scholar 

Savalia S, Acosta E, Emamian V, Savalia S, et al. Classification of cardiovascular disease using feature extraction and artificial neural networks. J Biosci Med. 2017;5(11):64–79. https://doi.org/10.4236/JBM.2017.511008.

Article  MATH  Google Scholar 

Kumar AS, Rekha R. Biomedical Signal Processing and Control, and undefined 2023. An improved hawk’s optimizer based learning algorithms for cardiovascular disease prediction. Elsevier AS Kumar, R Rekha Biomedical Signal Processing and Control, 2023•Elsevier, Accessed: Mar. 25, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1746809422008965

Thupakula S, Shankar S, Nimmala R, Ravula H, et al. Emerging biomarkers for the detection of cardiovascular diseases. Egypt Heart J. 2022;74:77. https://doi.org/10.1186/s43044-022-00317-2.

Article  Google Scholar 

Lane ES, Azarmehr N, Jevsikov J, Howard JP, et al. Multibeat echocardiographic phase detection using deep neural networks. Comput Biol Med. 2021;133: 104373. https://doi.org/10.1016/J.COMPBIOMED.2021.104373.

Article  Google Scholar 

Cardiovascular Disease dataset. https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset

Gopal S, Patro K, Sahu KK. Normalization: a pre-processing stage. IARJSET (2015); 20–22. https://doi.org/10.17148/iarjset.2015.2305.

Mukhametzyanov IZ. Normalization of multidimensional data for multi-criteria decision making problems: inversion, displacement, asymmetry. Springer Nature (2023); 348 https://doi.org/10.1007/978-981-15-1480-7_66

Carrara N, Ernst J. On the estimation of mutual information. Proceedings. 2020;33:31. https://doi.org/10.3390/PROCEEDINGS2019033031.

Article  MATH  Google Scholar 

Fang L, Han Zhao Pu, Wang MY, et al. Feature selection method based on mutual information and class separability for dimension reduction in multidimensional time series for clinical data. Biomed Signal Process Control. 2015;21:82–9. https://doi.org/10.1016/j.bspc.2015.05.011.

Article  MATH  Google Scholar 

Liu H, Sun J, Liu L, Zhang H. Feature selection with dynamic mutual information. Pattern Recogn. 2009;42(7):1330–9. https://doi.org/10.1016/J.PATCOG.2008.10.028.

Article  MATH  Google Scholar 

Li F, Li H, Niu B, Chen J. Privacy computing: concept, computing framework, and future development trends. Engineering. 2019;5(6):1179–92. https://doi.org/10.1016/J.ENG.2019.09.002.

Article  MATH  Google Scholar 

Savitha S, Rajiv Kannan A. A novel technique based on mutual information weighted feature selection to predict chronic kidney disease. J Intell Fuzzy Syst. 2023;45(1):491–504. https://doi.org/10.3233/JIFS-222401.

Article  Google Scholar 

Zou Q, Zeng J, Cao L, Ji R. A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing. 2016;173:346–54. https://doi.org/10.1016/J.NEUCOM.2014.12.123.

Article  MATH  Google Scholar 

Ho SW, Verdú S. On the interplay between conditional entropy and error probability. IEEE Trans Inf Theory. 2010;56(12):5930–42. https://doi.org/10.1109/TIT.2010.2080891.

Article  MathSciNet  MATH  Google Scholar 

Jahani MS, Aghamollaei G, Eftekhari M, Saberi-Movahed F. Unsupervised feature selection guided by orthogonal representation of feature space. Neurocomputing. 2023;516:61–76. https://doi.org/10.1016/J.NEUCOM.2022.10.030.

Article  Google Scholar 

Jadhav S, He H, Jenkins K. Information gain directed genetic algorithm wrapper feature selection for credit rating. Appl Soft Comput. 2018;69:541–53. https://doi.org/10.1016/J.ASOC.2018.04.033.

Article  MATH  Google Scholar 

Hinton G. Deep belief networks. Scholarpedia. 2009;4(5):5947. https://doi.org/10.4249/SCHOLARPEDIA.5947.

Article  MATH  Google Scholar 

Deng Li, Dong Yu. Deep learning: methods and applications. Found Trends® Signal Processing. 2014;7(34):197–387. https://doi.org/10.1561/2000000039.

Article  MathSciNet  MATH  Google Scholar 

Mathews SM, Kambhamettu C, Barner KE. A novel application of deep learning for single-lead ECG classification. Comput Biol Med. 2018;99:53–62. https://doi.org/10.1016/J.COMPBIOMED.2018.05.013.

Article  MATH  Google Scholar 

Tripathy BK, Maddikunta PKR, Pham QV, Gadekallu TR, et al. Harris Hawk optimization: a survey on variants and applications. Comput Intell Neurosci. 2022;2022:1. https://doi.org/10.1155/2022/2218594.

Article  Google Scholar 

Heidari AA, Mirjalili S, Faris H, Aljarah I, et al. Harris hawk’s optimization: algorithm and applications. Future Gen Computer Syst. 2019;97:849–72. https://doi.org/10.1016/J.FUTURE.2019.02.028.

Article  MATH  Google Scholar 

Fan Q, Chen Z, Xia Z. A novel quasi-reflected Harris hawk’s optimization algorithm for global optimization problems. Soft Comput. 2020;24(19):14825–43. https://doi.org/10.1007/S00500-020-04834-7/TABLES/12.

Article  MATH  Google Scholar 

Long W, Jiao J, Liang X, Ming X, et al. A velocity-guided Harris hawk’s optimizer for function optimization and fault diagnosis of wind turbine. Artif Intell Rev. 2023;56(3):2563–605. https://doi.org/10.1007/S10462-022-10233-1/TABLES/2.

Article  MATH