{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T18:45:02Z","timestamp":1772736302682,"version":"3.50.1"},"reference-count":84,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T00:00:00Z","timestamp":1550102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Research on predictions of breast cancer grows in the scientific community, providing data on studies in patient surveys. Predictive models link areas of medicine and artificial intelligence to collect data and improve disease assessments that affect a large part of the population, such as breast cancer. In this work, we used a hybrid artificial intelligence model based on concepts of neural networks and fuzzy systems to assist in the identification of people with breast cancer through fuzzy rules. The hybrid model can manipulate the data collected in medical examinations and identify patterns between healthy people and people with breast cancer with an acceptable level of accuracy. These intelligent techniques allow the creation of expert systems based on logical rules of the IF\/THEN type. To demonstrate the feasibility of applying fuzzy neural networks, binary pattern classification tests were performed where the dimensions of the problem are used for a model, and the answers identify whether or not the patient has cancer. In the tests, experiments were replicated with several characteristics collected in the examinations done by medical specialists. The results of the tests, compared to other models commonly used for this purpose in the literature, confirm that the hybrid model has a tremendous predictive capacity in the prediction of people with breast cancer maintaining acceptable levels of accuracy with good ability to act on false positives and false negatives, assisting the scientific milieu with its forecasts with the significant characteristic of interpretability of breast cancer. In addition to coherent predictions, the fuzzy neural network enables the construction of systems in high level programming languages to build support systems for physicians\u2019 actions during the initial stages of treatment of the disease with the fuzzy rules found, allowing the construction of systems that replicate the knowledge of medical specialists, disseminating it to other professionals.<\/jats:p>","DOI":"10.3390\/make1010028","type":"journal-article","created":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T11:54:13Z","timestamp":1550145253000},"page":"466-482","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer"],"prefix":"10.3390","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1845-5252","authenticated-orcid":false,"given":"Vin\u00edcius Jonathan","family":"Silva Ara\u00fajo","sequence":"first","affiliation":[{"name":"Information Systems Course\u2014Centro Universit\u00e1rio UNA de Betim, Minas Gerais 32.510-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1314-3441","authenticated-orcid":false,"given":"Augusto Junio","family":"Guimar\u00e3es","sequence":"additional","affiliation":[{"name":"Information Systems Course\u2014Centro Universit\u00e1rio UNA de Betim, Minas Gerais 32.510-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7343-5844","authenticated-orcid":false,"given":"Paulo Vitor","family":"de Campos Souza","sequence":"additional","affiliation":[{"name":"Information Systems Course\u2014Centro Universit\u00e1rio UNA de Betim, Minas Gerais 32.510-010, Brazil"},{"name":"Federal Center for Technological Education of Minas Gerais, Minas Gerais 30.421-169, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0040-8156","authenticated-orcid":false,"given":"Thiago Silva","family":"Rezende","sequence":"additional","affiliation":[{"name":"Information Systems Course\u2014Centro Universit\u00e1rio UNA de Betim, Minas Gerais 32.510-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9836-232X","authenticated-orcid":false,"given":"Vanessa Souza","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"Information Systems Course\u2014Centro Universit\u00e1rio UNA de Betim, Minas Gerais 32.510-010, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1038\/nrc1694","article-title":"Migrating cancer stem cells\u2014An integrated concept of malignant tumour progression","volume":"5","author":"Brabletz","year":"2005","journal-title":"Nat. Rev. Cancer"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1038\/415530a","article-title":"Gene expression profiling predicts clinical outcome of breast cancer","volume":"415","author":"Dai","year":"2002","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1001\/jama.1995.03520260071035","article-title":"Efficacy of screening mammography: A meta-analysis","volume":"273","author":"Kerlikowske","year":"1995","journal-title":"JAMA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1864","DOI":"10.1016\/S0140-6736(97)01004-0","article-title":"Sentinel-node biopsy to avoid axillary dissection in breast cancer with clinically negative lymph-nodes","volume":"349","author":"Veronesi","year":"1997","journal-title":"Lancet"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijmedinf.2017.10.022","article-title":"A biopsy of Breast Cancer mobile applications: State of the practice review","volume":"110","author":"Giunti","year":"2018","journal-title":"Int. J. Med. Informat."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S1526-8209(11)70627-9","article-title":"A novel approach toward development of a rapid blood test for breast cancer","volume":"4","author":"Vlahou","year":"2003","journal-title":"Clin. Breast Cancer"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Patr\u00edcio, M., Pereira, J., Cris\u00f3stomo, J., Matafome, P., Gomes, M., Sei\u00e7a, R., and Caramelo, F. (2018). Using Resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer, 18.","DOI":"10.1186\/s12885-017-3877-1"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zadeh, L.A. (1976). A fuzzy-algorithmic approach to the definition of complex or imprecise concepts. Systems Theory in the Social Sciences, Springer.","DOI":"10.1007\/978-3-0348-5495-5_11"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0165-0114(93)90181-G","article-title":"Fuzzy neural networks and neurocomputations","volume":"56","author":"Pedrycz","year":"1993","journal-title":"Fuzzy Sets Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1109\/TIE.2006.870880","article-title":"Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks","volume":"53","author":"Moreno","year":"2006","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cire\u015fan, D., Meier, U., and Schmidhuber, J. (arXiv, 2012). Multi-column deep neural networks for image classification, arXiv.","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/59.910780","article-title":"Neural networks for short-term load forecasting: A review and evaluation","volume":"16","author":"Hippert","year":"2001","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_13","first-page":"412","article-title":"A comparative study on feature selection in text categorization","volume":"97","author":"Yang","year":"1997","journal-title":"Icml"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es, A.J., Araujo, V.J.S., de Campos Souza, P.V., Araujo, V.S., and Rezende, T.S. (2018). Using Fuzzy Neural Networks to the Prediction of Improvement in Expert Systems for Treatment of Immunotherapy. Ibero-American Conference on Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-030-03928-8_19"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lin, F.J., Chen, S.G., and Hsu, C.W. (2018). Intelligent Backstepping Control Using Recurrent Feature Selection Fuzzy Neural Network for Synchronous Reluctance Motor Position Servo Drive System. IEEE Trans. Fuzzy Syst.","DOI":"10.1109\/TFUZZ.2018.2858749"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.renene.2017.12.023","article-title":"A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data","volume":"120","author":"Sharifian","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s11069-017-3122-x","article-title":"Fuzzy neural network and LLE Algorithm for forecasting precipitation in tropical cyclones: Comparisons with interpolation method by ECMWF and stepwise regression method","volume":"91","author":"Huang","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.3846\/tede.2018.6394","article-title":"Hybrid fuzzy neural network to predict price direction in the German DAX-30 index","volume":"24","author":"Guijarro","year":"2018","journal-title":"Technol. Econ. Dev. Econ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"De Campos Souza, P.V., and Torres, L.C.B. (2018). Regularized fuzzy neural network based on or neuron for time series forecasting. North American Fuzzy Information Processing Society Annual Conference, Springer.","DOI":"10.1007\/978-3-319-95312-0_2"},{"key":"ref_20","first-page":"1","article-title":"Pruning fuzzy neural networks based on unineuron for problems of classification of patterns","volume":"35","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1109\/21.256541","article-title":"ANFIS: Adaptive-network-based fuzzy inference system","volume":"23","author":"Jang","year":"1993","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, Y.W., and Lin, C.J. (2006). Combining SVMs with various feature selection strategies. Feature Extraction, Springer.","DOI":"10.1007\/978-3-540-35488-8_13"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Stewart, B., and Wild, C.P. (2014). World Cancer Report 2014, WHO.","DOI":"10.12968\/nuwa.2014.10.2.1142051"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3983","DOI":"10.1073\/pnas.0530291100","article-title":"Prospective identification of tumorigenic breast cancer cells","volume":"100","author":"Wicha","year":"2003","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105","DOI":"10.7326\/0003-4819-101-1-105","article-title":"Contemporary unorthodox treatments in cancer medicine: A study of patients, treatments, and practitioners","volume":"101","author":"Cassileth","year":"1984","journal-title":"Ann. Intern. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3322\/caac.21203","article-title":"Breast cancer statistics, 2013","volume":"64","author":"DeSantis","year":"2014","journal-title":"CA Cancer J. Clin."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"678","DOI":"10.7326\/0003-4819-137-8-200210150-00013","article-title":"Breast cancer in men","volume":"137","author":"Giordano","year":"2002","journal-title":"Ann. Intern. Med."},{"key":"ref_29","unstructured":"(2019, February 11). Breast Cancer. Available online: https:\/\/pt.wikipedia.org\/wiki\/Ficheiro:Diagram_1_of_2_showing_stage_2A_breast_cancer_CRUK_003.svg."},{"key":"ref_30","first-page":"41","article-title":"A comprehensive foundation","volume":"2","author":"Haykin","year":"2004","journal-title":"Neural Netw."},{"key":"ref_31","unstructured":"(2019, February 11). The Artificial Neural Networks Handbook: Part 1. Available online: https:\/\/medium.com\/coinmonks\/the-artificial-neural-networks-handbook-part-1-f9ceb0e376b4."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/0020-0255(75)90036-5","article-title":"The concept of a linguistic variable and its application to approximate reasoning\u2014I","volume":"8","author":"Zadeh","year":"1975","journal-title":"Inf. Sci."},{"key":"ref_33","unstructured":"Cherri, A.C., Junior, D.J.A., and da Silva, I.N. (2019, February 11). Inferencia fuzzy para o problema de corte de estoque com sobras aproveitaveis de material. Available online: http:\/\/www.scielo.br\/scielo.php?script=sci_arttext&pid=S0101-74382011000100011."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jang, J.S.R., Sun, C.T., and Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing; A Computational Approach to Learning and Machine Intelligence, Prentice Hall.","DOI":"10.1109\/TAC.1997.633847"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0165-0114(91)90065-X","article-title":"Evaluation of fuzzy linear regression models","volume":"39","author":"Savic","year":"1991","journal-title":"Fuzzy Sets Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1109\/34.75517","article-title":"Neurocomputations in relational systems","volume":"13","author":"Pedrycz","year":"1991","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lemos, A., Caminhas, W., and Gomide, F. (2010, January 12\u201314). New uninorm-based neuron model and fuzzy neural networks. Proceedings of the 2010 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), Toronto, ON, Canada.","DOI":"10.1109\/NAFIPS.2010.5548195"},{"key":"ref_38","first-page":"1775","article-title":"Mammary lymphoscintigraphy in breast cancer","volume":"36","author":"Uren","year":"1995","journal-title":"J. Nuclear Med. Off. Publ. Soc. Nuclear Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"699","DOI":"10.2214\/ajr.162.3.8109525","article-title":"Computer vision and artificial intelligence in mammography","volume":"162","author":"Vyborny","year":"1994","journal-title":"Am. J. Roentgenol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.trsl.2017.10.010","article-title":"Digital image analysis in breast pathology\u2014From image processing techniques to artificial intelligence","volume":"194","author":"Robertson","year":"2018","journal-title":"Transl. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat. Rev. Cancer"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/S0933-3657(02)00028-3","article-title":"An evolutionary artificial neural networks approach for breast cancer diagnosis","volume":"25","author":"Abbass","year":"2002","journal-title":"Artif. Intell. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.artmed.2004.07.002","article-title":"Predicting breast cancer survivability: A comparison of three data mining methods","volume":"34","author":"Delen","year":"2005","journal-title":"Artif. Intell. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/S0933-3657(99)00041-X","article-title":"Generating concise and accurate classification rules for breast cancer diagnosis","volume":"18","author":"Setiono","year":"2000","journal-title":"Artif. Intell. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"9573","DOI":"10.1016\/j.eswa.2011.01.167","article-title":"WBCD breast cancer database classification applying artificial metaplasticity neural network","volume":"38","author":"Andina","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, Y., Kohlberger, T., Norouzi, M., Dahl, G.E., Smith, J.L., Mohtashamian, A., Olson, N., Peng, L.H., Hipp, J.D., and Stumpe, M.C. (2018). Artificial Intelligence\u2013Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists. Arch. Pathol. Lab. Med.","DOI":"10.5858\/arpa.2018-0147-OA"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Khan, M.M., Mendes, A., and Chalup, S.K. (2018). Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson\u2019s disease prediction. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0192192"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/S0933-3657(99)00019-6","article-title":"A fuzzy-genetic approach to breast cancer diagnosis","volume":"17","author":"Sipper","year":"1999","journal-title":"Artif. Intell. Med."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1083\/jcb.201709092","article-title":"A Numb\u2013Mdm2 fuzzy complex reveals an isoform-specific involvement of Numb in breast cancer","volume":"217","author":"Colaluca","year":"2018","journal-title":"J. Cell Biol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3240","DOI":"10.1016\/j.eswa.2008.01.009","article-title":"Support vector machines combined with feature selection for breast cancer diagnosis","volume":"36","author":"Akay","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1016\/j.dsp.2006.10.008","article-title":"Breast cancer diagnosis using least square support vector machine","volume":"17","author":"Polat","year":"2007","journal-title":"Dig. Signal Process."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Cire\u015fan, D.C., Giusti, A., Gambardella, L.M., and Schmidhuber, J. (2013). Mitosis detection in breast cancer histology images with deep neural networks. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-642-40763-5_51"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1148\/radiol.2018182116","article-title":"Deep Learning for Mammographic Breast Density Assessment and Beyond","volume":"290","author":"Chan","year":"2019","journal-title":"Radiology"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Guimaraes, A.J., Ara\u00fajo, V.J., de Oliveira Batista, L., Souza, P.V.C., Ara\u00fajo, V., and Rezende, T.S. (2018, January 22\u201325). Using Fuzzy Neural Networks to Improve Prediction of Expert Systems for Detection of Breast Cancer. Proceedings of the XV Encontro Nacional de Intelig\u00eancia Artificial e Computacional (ENIAC), Sao Paulo, SP, Brazil.","DOI":"10.5753\/eniac.2018.4468"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"212","DOI":"10.11648\/j.acm.20180704.15","article-title":"Performance Evaluation of Machine Learning Methods for Breast Cancer Prediction","volume":"7","author":"Li","year":"2018","journal-title":"Appl. Comput. Math."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Polat, K., and Sent\u00fcrk, U. (2018, January 19\u201321). A Novel ML Approach to Prediction of Breast Cancer: Combining of mad normalization, KMC based feature weighting and AdaBoostM1 classifier. Proceedings of the 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey.","DOI":"10.1109\/ISMSIT.2018.8567245"},{"key":"ref_57","unstructured":"Dular, L. (2018). Statistical Comparison of Machine Learning Algorithms with Respect to Multiple Performance Measures: Master\u2019s Thesis. [Ph.D. Thesis, Univerza v Ljubljani, Fakulteta za Matematiko in Fiziko]."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Livieris, I. (2019). Improving the Classification Efficiency of an ANN Utilizing a New Training Methodology. Informatics, 6.","DOI":"10.3390\/informatics6010001"},{"key":"ref_59","unstructured":"Patterson, D.W. (1998). Redes Neurais Artificiais: Teoria e Aplica\u00e7\u00f5es, Prentice Hall PTR."},{"key":"ref_60","first-page":"2985","article-title":"Regularized Fuzzy Neural Networks for Pattern Classification Problems","volume":"13","author":"Souza","year":"2018","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Gao, J., Wang, Z., Yang, Y., Zhang, W., Tao, C., Guan, J., and Rao, N. (2013). A novel approach for lie detection based on F-score and extreme learning machine. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0064704"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1016\/j.eswa.2009.05.075","article-title":"Multi-class f-score feature selection approach to classification of obstructive sleep apnea syndrome","volume":"37","author":"Polat","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/5.58337","article-title":"Backpropagation through time: What it does and how to do it","volume":"78","author":"Werbos","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_64","unstructured":"Albert, A. (1972). Regression and the Moore-Penrose Pseudoinverse, Elsevier."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"De Campos Souza, P.V., Guimaraes, A.J., Ara\u00fajo, V.S., Rezende, T.S., and Ara\u00fajo, V.J.S. (2018, January 7\u20139). Method of pruning the hidden layer of the extreme learning machine based on correlation coefficient. Proceedings of the 5th IEEE Latin American Conference on Computational Intelligence LA-CCI, Guadalajara, Mexico.","DOI":"10.1109\/LA-CCI.2018.8625247"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.eswa.2017.12.015","article-title":"Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces","volume":"96","author":"Zhang","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.dss.2008.07.009","article-title":"Sales forecasting using extreme learning machine with applications in fashion retailing","volume":"46","author":"Sun","year":"2008","journal-title":"Decis. Support Syst."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"6961","DOI":"10.1109\/TSG.2018.2807845","article-title":"Probabilistic Load Forecasting using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine","volume":"9","author":"Rafiei","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.eswa.2017.10.053","article-title":"Data decomposition based fast reduced kernel extreme learning machine for currency exchange rate forecasting and trend analysis","volume":"96","author":"Das","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s12667-016-0227-3","article-title":"Wavelet transform and Kernel-based extreme learning machine for electricity price forecasting","volume":"9","author":"Zhang","year":"2018","journal-title":"Energy Syst."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"012086","DOI":"10.1088\/1757-899X\/342\/1\/012086","article-title":"Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation","volume":"Volume 342","author":"Janahiraman","year":"2018","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"8472","DOI":"10.1109\/JSEN.2018.2866708","article-title":"Fault Diagnosis Based on Weighted Extreme Learning Machine With Wavelet Packet Decomposition and KPCA","volume":"18","author":"Hu","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.energy.2018.08.180","article-title":"A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction","volume":"164","author":"Xu","year":"2018","journal-title":"Energy"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.energy.2018.06.220","article-title":"Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine","volume":"160","author":"Pan","year":"2018","journal-title":"Energy"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.eswa.2018.03.024","article-title":"Improving extreme learning machine by competitive swarm optimization and its application for medical diagnosis problems","volume":"104","author":"Eshtay","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.comnet.2018.11.002","article-title":"Collaborative extreme learning machine with a confidence interval for P2P learning in healthcare","volume":"149","author":"Rongjun","year":"2019","journal-title":"Comput. Netw."},{"key":"ref_78","unstructured":"De Campos Souza, P.V., and Guimar\u00e3es, A.J. (2018, January 2\u20134). Utilizando redes neurais nebulosas para melhoria na predi\u00e7\u00e3o de sistemas especialistas para tratamento de crioterapia. Proceedings of the Anais- I Congresso Internacional Cidadania, Ci\u00eancia, Direito e Sa\u00fade: Reflex\u00f5es Transdisciplinares, Barreiro, Brazil. (In Portuguese)."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newslett."},{"key":"ref_80","first-page":"41","article-title":"An empirical study of the naive Bayes classifier","volume":"Volume 3","author":"Rish","year":"2001","journal-title":"IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"ref_82","unstructured":"LaValle, S.M. (1998). Rapidly-exploring random trees: A new tool for path planning. Computer Science Dept. Research Report 9811, CiteSeerX."},{"key":"ref_83","unstructured":"Witten, I.H., Frank, E., Trigg, L.E., Hall, M.A., Holmes, G., and Cunningham, S.J. (1999). Weka: Practical Machine Learning Tools and Techniques with Java Implementations, Citeseer."},{"key":"ref_84","unstructured":"Quinlan, J.R. (2014). C4. 5: Programs for Machine Learning, Elsevier."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/1\/28\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:32:12Z","timestamp":1760185932000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/1\/28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,14]]},"references-count":84,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["make1010028"],"URL":"https:\/\/doi.org\/10.3390\/make1010028","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,14]]}}}