{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T23:37:44Z","timestamp":1772667464595,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,11,28]],"date-time":"2020-11-28T00:00:00Z","timestamp":1606521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>To make healthcare available and easily accessible, the Internet of Things (IoT), which paved the way to the construction of smart cities, marked the birth of many smart applications in numerous areas, including healthcare. As a result, smart healthcare applications have been and are being developed to provide, using mobile and electronic technology, higher diagnosis quality of the diseases, better treatment of the patients, and improved quality of lives. Since smart healthcare applications that are mainly concerned with the prediction of healthcare data (like diseases for example) rely on predictive healthcare data analytics, it is imperative for such predictive healthcare data analytics to be as accurate as possible. In this paper, we will exploit supervised machine learning methods in classification and regression to improve the performance of the traditional Random Forest on healthcare datasets, both in terms of accuracy and classification\/regression speed, in order to produce an effective and efficient smart healthcare application, which we have termed eGAP. eGAP uses the evolutionary game theoretic approach replicator dynamics to evolve a Random Forest ensemble. Trees of high resemblance in an initial Random Forest are clustered, and then clusters grow and shrink by adding and removing trees using replicator dynamics, according to the predictive accuracy of each subforest represented by a cluster of trees. All clusters have an initial number of trees that is equal to the number of trees in the smallest cluster. Cluster growth is performed using trees that are not initially sampled. The speed and accuracy of the proposed method have been demonstrated by an experimental study on 10 classification and 10 regression medical datasets.<\/jats:p>","DOI":"10.3390\/bdcc4040037","type":"journal-article","created":{"date-parts":[[2020,11,29]],"date-time":"2020-11-29T21:00:57Z","timestamp":1606683657000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7225-0373","authenticated-orcid":false,"given":"Khaled","family":"Fawagreh","sequence":"first","affiliation":[{"name":"Department of Information Technology, Prince Mohammad Bin Fahd University, Dhahran 34754, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0339-4474","authenticated-orcid":false,"given":"Mohamed Medhat","family":"Gaber","sequence":"additional","affiliation":[{"name":"School of Computing and Digital Technology, Birmingham City University, Birmingham B5 5JU, UK"},{"name":"Faculty of Computer Science and Engineering, Galala University, Attaka 43511, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lytras, M., and Visvizi, A. (2018). Who uses smart city services and what to make of it: Toward interdisciplinary smart cities research. Sustainability, 10.","DOI":"10.3390\/su10061998"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chui, K., Lytras, M., and Visvizi, A. (2018). Energy Sustainability in Smart Cities: Artificial intelligence, smart monitoring, and optimization of energy consumption. Energies, 11.","DOI":"10.3390\/en11112869"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lytras, M., Visvizi, A., Daniela, L., Sarirete, A., and Ordonez De Pablos, P. (2018). Social networks research for sustainable smart education. Sustainability, 10.","DOI":"10.3390\/su10092974"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"119371","DOI":"10.1016\/j.techfore.2018.07.009","article-title":"An evaluation system based on the self-organizing system framework of smart cities: A case study of smart transportation systems in China","volume":"153","author":"Yan","year":"2018","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chui, K.T., Alhalabi, W., Pang, S.S.H., Pablos, P.O.D., Liu, R.W., and Zhao, M. (2017). Disease diagnosis in smart healthcare: Innovation, technologies and applications. Sustainability, 9.","DOI":"10.3390\/su9122309"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lytras, M.D., Chui, K.T., and Visvizi, A. (2019). Data Analytics in Smart Healthcare: The Recent Developments and Beyond. Appl. Sci., 9.","DOI":"10.3390\/app9142812"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1080\/21642583.2014.956265","article-title":"Random forests: From early developments to recent advancements","volume":"2","author":"Fawagreh","year":"2014","journal-title":"Syst. Sci. Control. Eng. Open Access J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fawagreh, K., Gaber, M.M., and Elyan, E. (2015, January 15\u201317). Club-drf: A clustering approach to extreme pruning of random forests. Proceedings of the International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, UK.","DOI":"10.1007\/978-3-319-25032-8_4"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fawagreh, K., and Gaber, M.M. (2020). Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach. Computing, 1\u201312.","DOI":"10.1007\/s00607-019-00785-6"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Menger, V., Scheepers, F., and Spruit, M. (2018). Comparing deep learning and classical machine learning approaches for predicting inpatient violence incidents from clinical text. Appl. Sci., 8.","DOI":"10.3390\/app8060981"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ijaz, M., Alfian, G., Syafrudin, M., and Rhee, J. (2018). Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest. Appl. Sci., 8.","DOI":"10.3390\/app8081325"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mun, S., Park, J., Dritschilo, A., Collins, S., Suy, S., Choi, I., and Rho, M. (2018). The Prostate Clinical Outlook (PCO) Classifier Application for Predicting Biochemical Recurrences in Patients Treated by Stereotactic Body Radiation Therapy (SBRT). Appl. Sci., 8.","DOI":"10.3390\/app8091620"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, C., and Zhang, W. (2018). Data Analysis and Forecasting of Tuberculosis Prevalence Rates for Smart Healthcare Based on a Novel Combination Model. Appl. Sci., 8.","DOI":"10.3390\/app8091693"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100178","DOI":"10.1016\/j.imu.2019.100178","article-title":"Prediction of kidney disease stages using data mining algorithms","volume":"15","author":"Rady","year":"2019","journal-title":"Inform. Med. Unlocked"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.mri.2019.05.017","article-title":"Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa","volume":"64","author":"Romeo","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"ref_16","first-page":"118","article-title":"Health data analytics using scalable logistic regression with stochastic gradient descent","volume":"10","author":"Manogaran","year":"2018","journal-title":"Int. J. Adv. Intell. Paradig."},{"key":"ref_17","first-page":"79","article-title":"An Optimized Sub Group Partition based Healthcare Data Mining in Big Data","volume":"4","author":"Nagarajan","year":"2018","journal-title":"Int. J. Innov. Res. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"19905","DOI":"10.1007\/s11042-019-7327-8","article-title":"A healthcare monitoring system using random forest and internet of things (IoT)","volume":"78","author":"Kaur","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e1292","DOI":"10.1002\/widm.1292","article-title":"Internet of Things and data mining: From applications to techniques and systems","volume":"9","author":"Gaber","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e1341","DOI":"10.1002\/widm.1341","article-title":"Internet of Things and data analytics: A current review","volume":"10","author":"Mohindru","year":"2020","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_22","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_23","unstructured":"Ho, T.K. (1995, January 14\u201316). Random decision forests. Proceedings of the Third International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1162\/neco.1997.9.7.1545","article-title":"Shape quantization and recognition with randomized trees","volume":"9","author":"Amit","year":"1997","journal-title":"Neural Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fawgreh, K., Gaber, M.M., and Elyan, E. (2015, January 15\u201317). A replicator dynamics approach to collective feature engineering in random forests. Proceedings of the International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, UK.","DOI":"10.1007\/978-3-319-25032-8_2"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fawagreh, K., Gaber, M.M., and Elyan, E. (2014, January 10\u201312). Diversified random forests using random subspaces. Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning, Salamanca, Spain.","DOI":"10.1007\/978-3-319-10840-7_11"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1023\/A:1022859003006","article-title":"Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy","volume":"51","author":"Kuncheva","year":"2003","journal-title":"Mach. Learn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.inffus.2004.04.004","article-title":"Diversity creation methods: A survey and categorisation","volume":"6","author":"Brown","year":"2005","journal-title":"Inf. Fusion"},{"key":"ref_30","first-page":"1","article-title":"Accuracy and diversity in ensembles of text categorisers","volume":"9","author":"Adeva","year":"2005","journal-title":"CLEI Electron. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10994-006-9449-2","article-title":"An analysis of diversity measures","volume":"65","author":"Tang","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/0022-5193(83)90445-9","article-title":"Replicator dynamics","volume":"100","author":"Schuster","year":"1983","journal-title":"J. Theor. Biol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0025-5564(78)90077-9","article-title":"Evolutionary stable strategies and game dynamics","volume":"40","author":"Taylor","year":"1978","journal-title":"Math. Biosci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1090\/S0273-0979-03-00988-1","article-title":"Evolutionary game dynamics","volume":"40","author":"Hofbauer","year":"2003","journal-title":"Bull. Am. Math. Soc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1007\/s11538-010-9608-2","article-title":"Local replicator dynamics: A simple link between deterministic and stochastic models of evolutionary game theory","volume":"73","author":"Hilbe","year":"2011","journal-title":"Bull. Math. Biol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.jtbi.2010.08.009","article-title":"Replicator dynamics of reward & reputation in public goods games","volume":"267","author":"Hauert","year":"2010","journal-title":"J. Theor. Biol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1126\/science.1093411","article-title":"Evolutionary dynamics of biological games","volume":"303","author":"Nowak","year":"2004","journal-title":"Science"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1006\/jtbi.2002.3067","article-title":"Replicator dynamics for optional public good games","volume":"218","author":"Hauert","year":"2002","journal-title":"J. Theor. Biol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.plrev.2009.08.001","article-title":"Evolutionary game theory: Temporal and spatial effects beyond replicator dynamics","volume":"6","author":"Roca","year":"2009","journal-title":"Phys. Life Rev."},{"key":"ref_40","unstructured":"Asuncion, A., and Newman, D. (2020, November 26). UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/ml\/index.php."},{"key":"ref_41","unstructured":"Sharma, A. (2020, November 26). MLData. Available online: https:\/\/www.mldata.io\/."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/4\/4\/37\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:39:01Z","timestamp":1760179141000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/4\/4\/37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,28]]},"references-count":41,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["bdcc4040037"],"URL":"https:\/\/doi.org\/10.3390\/bdcc4040037","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,28]]}}}