{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T15:27:21Z","timestamp":1770996441107,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,14]],"date-time":"2024-01-14T00:00:00Z","timestamp":1705190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in preventing adverse outcomes and improving patient well-being, thereby creating a growing demand for intelligent approaches to predict heart disease effectively. This paper introduces an ensemble heuristic\u2013metaheuristic feature fusion learning (EHMFFL) algorithm for heart disease diagnosis using tabular data. Within the EHMFFL algorithm, a diverse ensemble learning model is crafted, featuring different feature subsets for each heterogeneous base learner, including support vector machine, K-nearest neighbors, logistic regression, random forest, naive bayes, decision tree, and XGBoost techniques. The primary objective is to identify the most pertinent features for each base learner, leveraging a combined heuristic\u2013metaheuristic approach that integrates the heuristic knowledge of the Pearson correlation coefficient with the metaheuristic-driven grey wolf optimizer. The second objective is to aggregate the decision outcomes of the various base learners through ensemble learning. The performance of the EHMFFL algorithm is rigorously assessed using the Cleveland and Statlog datasets, yielding remarkable results with an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art techniques in heart disease diagnosis. These findings underscore the potential of the EHMFFL algorithm in enhancing diagnostic accuracy for heart disease and providing valuable support to clinicians in making more informed decisions regarding patient care.<\/jats:p>","DOI":"10.3390\/a17010034","type":"journal-article","created":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T04:54:38Z","timestamp":1705294478000},"page":"34","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Ensemble Heuristic\u2013Metaheuristic Feature Fusion Learning for Heart Disease Diagnosis Using Tabular Data"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7370-4760","authenticated-orcid":false,"given":"Mohammad","family":"Shokouhifar","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Shahid Beheshti University, Tehran 1983969411, Iran"}]},{"given":"Mohamad","family":"Hasanvand","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2945-9387","authenticated-orcid":false,"given":"Elaheh","family":"Moharamkhani","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Institute of Higher Education Saeb, Abhar 3697345619, Iran"},{"name":"IRO, Computer Science Department, University of Halabja, Halabja 46018, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0709-3591","authenticated-orcid":false,"given":"Frank","family":"Werner","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics, Otto-von-Guericke University, 39016 Magdeburg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,14]]},"reference":[{"key":"ref_1","first-page":"531","article-title":"Heart disease detection using core machine learning and deep learning techniques: A comparative study","volume":"11","author":"Das","year":"2020","journal-title":"Int. J. Emerg. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hasan, T.T., Jasim, M.H., and Hashim, I.A. (2018, January 19\u201320). FPGA design and hardware implementation of heart disease diagnosis system based on NVG-RAM classifier. Proceedings of the 2018 3rd Scientific Conference of Electrical Engineering (SCEE), Baghdad, Iraq.","DOI":"10.1109\/SCEE.2018.8684125"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Rahman, A.U., Saeed, M., Mohammed, M.A., Jaber, M.M., and Garcia-Zapirain, B. (2022). A novel fuzzy parameterized fuzzy hypersoft set and riesz summability approach based decision support system for diagnosis of heart diseases. Diagnostics, 12.","DOI":"10.3390\/diagnostics12071546"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Javid, I., Alsaedi AK, Z., and Ghazali, R. (2020). Enhanced accuracy of heart disease prediction using machine learning and recurrent neural networks ensemble majority voting method. Int. J. Adv. Comput. Sci. Appl., 11.","DOI":"10.14569\/IJACSA.2020.0110369"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Muhsen, D.K., Khairi TW, A., and Alhamza NI, A. (2021, January 19). Machine learning system using modified random forest algorithm. Proceedings of the Intelligent Systems and Networks (ICISN 2021), Hanoi, Vietnam.","DOI":"10.1007\/978-981-16-2094-2_61"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mastoi QU, A., Wah, T.Y., Mohammed, M.A., Iqbal, U., Kadry, S., Majumdar, A., and Thinnukool, O. (2022). Novel DERMA fusion technique for ECG heartbeat classification. Life, 12.","DOI":"10.3390\/life12060842"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.eswa.2012.07.032","article-title":"Computational intelligence for heart disease diagnosis: A medical knowledge driven approach","volume":"40","author":"Nahar","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_8","unstructured":"Lee, H.G., Noh, K.Y., and Ryu, K.H. (2007, January 22\u201325). Mining biosignal data: Coronary artery disease diagnosis using linear and nonlinear features of HRV. Proceedings of the Emerging Technologies in Knowledge Discovery and Data Mining: PAKDD 2007 International Workshops, Nanjing, China. Revised Selected Papers 11."},{"key":"ref_9","first-page":"1157","article-title":"Study of heart disease prediction using data mining","volume":"4","author":"Sudhakar","year":"2014","journal-title":"Int. J. Adv. Res. Comput. Sci. Softw. Eng."},{"key":"ref_10","first-page":"25","article-title":"Heart beat classification using particle swarm optimization","volume":"5","author":"Khazaee","year":"2013","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xing, Y., Wang, J., and Zhao, Z. (2007, January 21\u201323). Combination data mining methods with new medical data to predicting outcome of coronary heart disease. Proceedings of the 2007 International Conference on Convergence Information Technology (ICCIT 2007), Gwangju, Republic of Korea.","DOI":"10.1109\/ICCIT.2007.204"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Schapire, R.E., and Singer, Y. (1998, January 24\u201326). Improved boosting algorithms using confidence-rated predictions. Proceedings of the 11th Annual Conference on Computational Learning Theory, Madison, WI, USA.","DOI":"10.1145\/279943.279960"},{"key":"ref_14","first-page":"1","article-title":"Coronary heart disease diagnosis using deep neural networks","volume":"9","author":"Miao","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1134\/S0361768818060129","article-title":"A machine learning framework for feature selection in heart disease classification using improved particle swarm optimization with support vector machine classifier","volume":"44","author":"Vijayashree","year":"2018","journal-title":"Program. Comput. Softw."},{"key":"ref_16","first-page":"1638","article-title":"Predicting the risk of heart disease using advanced machine learning approach","volume":"7","author":"Waigi","year":"2020","journal-title":"Eur. J. Mol. Clin. Med"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.future.2019.10.043","article-title":"HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments","volume":"104","author":"Tuli","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"012072","DOI":"10.1088\/1757-899X\/1022\/1\/012072","article-title":"Heart disease prediction using machine learning algorithms","volume":"1022","author":"Jindal","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sarra, R.R., Dinar, A.M., Mohammed, M.A., and Abdulkareem, K.H. (2022). Enhanced heart disease prediction based on machine learning and \u03c72 statistical optimal feature selection model. Designs, 6.","DOI":"10.3390\/designs6050087"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1080\/10255842.2021.1955360","article-title":"A new variant of deep belief network assisted with optimal feature selection for heart disease diagnosis using IoT wearable medical devices","volume":"25","author":"Brindha","year":"2022","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100203","DOI":"10.1016\/j.imu.2019.100203","article-title":"Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques","volume":"16","author":"Jeeva","year":"2019","journal-title":"Inform. Med. Unlocked"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.inffus.2020.06.008","article-title":"A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion","volume":"63","author":"Ali","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100655","DOI":"10.1016\/j.imu.2021.100655","article-title":"Early detection of coronary heart disease using ensemble techniques","volume":"26","author":"Shorewala","year":"2021","journal-title":"Inform. Med. Unlocked"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e6726","DOI":"10.1002\/cpe.6726","article-title":"SI-EDTL: Swarm intelligence ensemble deep transfer learning for multiple vehicle detection in UAV images","volume":"34","author":"Shokouhifar","year":"2022","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shokouhifar, A., Shokouhifar, M., Sabbaghian, M., and Soltanian-Zadeh, H. (2023). Swarm intelligence empowered three-stage ensemble deep learning for arm volume measurement in patients with lymphedema. Biomed. Signal Process. Control., 85.","DOI":"10.1016\/j.bspc.2023.105027"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s40860-021-00152-3","article-title":"Innovative feature selection and classification model for heart disease prediction","volume":"8","author":"Nagarajan","year":"2022","journal-title":"J. Reliab. Intell. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1007\/s00500-020-05253-4","article-title":"Feature optimization by discrete weights for heart disease prediction using supervised learning","volume":"25","year":"2021","journal-title":"Soft Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"23808","DOI":"10.1109\/ACCESS.2022.3153047","article-title":"Comparative study of optimum medical diagnosis of human heart disease using machine learning technique with and without sequential feature selection","volume":"10","author":"Ahmad","year":"2022","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"100060","DOI":"10.1016\/j.health.2022.100060","article-title":"Analyzing the impact of feature selection on the accuracy of heart disease prediction","volume":"2","author":"Pathan","year":"2022","journal-title":"Healthc. Anal."},{"key":"ref_30","first-page":"6260022","article-title":"Heart disease prediction based on the embedded feature selection method and deep neural network","volume":"2021","author":"Zhang","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_31","unstructured":"(1989, August 01). Heart Disease. UCI Machine Learning Repository. Available online: https:\/\/doi.org\/10.24432\/C52P4X."},{"key":"ref_32","unstructured":"(1993, February 13). Statlog (Heart). UCI Machine Learning Repository. Available online: https:\/\/doi.org\/10.24432\/C57303."},{"key":"ref_33","unstructured":"Jensen, R. (2005). Combining Rough and Fuzzy Sets for Feature Selection. [Ph.D. Thesis, University of Edinburgh]."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Seyyedabbasi, A. (2023). Binary Sand Cat Swarm Optimization Algorithm for Wrapper Feature Selection on Biological Data. Biomimetics, 8.","DOI":"10.3390\/biomimetics8030310"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shokouhifar, M., Sohrabi, M., Rabbani, M., Molana SM, H., and Werner, F. (2023). Sustainable Phosphorus Fertilizer Supply Chain Management to Improve Crop Yield and P Use Efficiency Using an Ensemble Heuristic\u2013Metaheuristic Optimization Algorithm. Agronomy, 13.","DOI":"10.3390\/agronomy13020565"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"101462","DOI":"10.1016\/j.seps.2022.101462","article-title":"Sustainable inventory management in blood banks considering health equity using a combined metaheuristic-based robust fuzzy stochastic programming","volume":"86","author":"Sohrabi","year":"2023","journal-title":"Socio-Econ. Plan. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xie, W., Li, W., Zhang, S., Wang, L., Yang, J., and Zhao, D. (2022). A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data. BMC Bioinform., 23.","DOI":"10.1186\/s12859-022-04848-y"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1098\/rsta.1894.0003","article-title":"Contributions to the mathematical theory of evolution","volume":"185","author":"Pearson","year":"1894","journal-title":"Philos. Trans. R. Soc. Lond. A"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey wolf optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Grover, P., Chaturvedi, K., Zi, X., Saxena, A., Prakash, S., Jan, T., and Prasad, M. (2023). Ensemble Transfer Learning for Distinguishing Cognitively Normal and Mild Cognitive Impairment Patients Using MRI. Algorithms, 16.","DOI":"10.3390\/a16080377"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/1\/34\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:46:24Z","timestamp":1760103984000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/1\/34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,14]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["a17010034"],"URL":"https:\/\/doi.org\/10.3390\/a17010034","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,14]]}}}