{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:36:41Z","timestamp":1785404201045,"version":"3.56.0"},"reference-count":64,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Secretar\u00eda de Ciencia, Humanidades, Tecnolog\u00eda e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["331870"],"award-info":[{"award-number":["331870"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"award":["331870"],"award-info":[{"award-number":["331870"]}],"id":[{"id":"https:\/\/ror.org\/059ex5q34","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Multiple sclerosis (MS) is an inflammatory disease of the central nervous system (CNS) that impacts nearly 3 million people worldwide. While the etiology and pathogenesis of MS are not yet fully understood, current evidence suggests that it results from complex interactions between genetic and environmental conditions. Clarifying the autoimmune mechanisms underlying MS remains a central objective in the development of effective therapeutic strategies. Interferon-beta (IFN-\u03b2) is one of the most frequently prescribed disease-modifying treatments for individuals with MS. However, despite its established efficacy, recent studies report that approximately 30\u201350% of patients exhibit inadequate response to IFN-\u03b2, largely due to genetic variability. Machine learning (ML), a branch of artificial intelligence (AI), employs data-driven computational models to enhance predictive accuracy and classification. In recent MS research, unsupervised learning techniques such as hierarchical clustering and K-means have been applied for classification purposes. However, these methods often fail to yield optimal solutions because they require numerous arbitrary decisions and perform adequately only when datasets contain clusters of similar sizes and lack significant outliers. Fuzzy systems (FSs) are designed to model complex, ambiguous real-world phenomena. In this study, an AI algorithm incorporating a fuzzy system, informed by expert neurologist input, is proposed to enhance the assignment of unknown class labels related to IFN-\u03b2 response in MS patients. Additionally, a genetic algorithm (GA) is introduced to identify optimal solutions within the search space, facilitating hyperparameter optimization of a deep learning (DL) model trained with genetic biomarkers to identify patients likely to benefit from this therapy. Experimental results demonstrate that the fuzzy system achieved 80% classification efficiency, in contrast to 64% with conventional hierarchical clustering. Furthermore, an artificial neural network (ANN) model, with hyperparameters optimized by the GA, achieved an accuracy of 0.8\u20131.0, surpassing the multi-layer perceptron (MLP), which achieved 0.6\u20130.8 accuracy using conventional tuning methods.<\/jats:p>","DOI":"10.3390\/bioengineering13050523","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T10:47:28Z","timestamp":1777546048000},"page":"523","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Algorithm Based on Genetics to Predict Responses to Interferon-Beta Treatment in Multiple Sclerosis Patients"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9719-829X","authenticated-orcid":false,"given":"Edgar Rafael","family":"Ponce de Le\u00f3n-S\u00e1nchez","sequence":"first","affiliation":[{"name":"Facultad de Inform\u00e1tica, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76230, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9173-0732","authenticated-orcid":false,"given":"Jorge Domingo","family":"Mendiola-Santiba\u00f1ez","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76010, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9663-8089","authenticated-orcid":false,"given":"Omar Arturo","family":"Dom\u00ednguez-Ram\u00edrez","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Tecnolog\u00edas de Informaci\u00f3n y Sistemas, Universidad Aut\u00f3noma del Estado de Hidalgo, Pachuca 42039, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7711-9585","authenticated-orcid":false,"given":"Ana Marcela","family":"Herrera-Navarro","sequence":"additional","affiliation":[{"name":"Facultad de Inform\u00e1tica, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76230, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9911-4328","authenticated-orcid":false,"given":"Alberto","family":"V\u00e1zquez-Cervantes","sequence":"additional","affiliation":[{"name":"Centro de Ingenier\u00eda y Desarrollo Industrial, Quer\u00e9taro 76125, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0827-6645","authenticated-orcid":false,"given":"Hugo","family":"Jim\u00e9nez-Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Facultad de Inform\u00e1tica, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76230, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7541-7471","authenticated-orcid":false,"given":"Jos\u00e9 Alfredo","family":"Acu\u00f1a-Garc\u00eda","sequence":"additional","affiliation":[{"name":"Facultad de Inform\u00e1tica, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76230, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7443-1842","authenticated-orcid":false,"given":"Rafael","family":"Duarte-P\u00e9rez","sequence":"additional","affiliation":[{"name":"Facultad de Inform\u00e1tica, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76230, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1304-6791","authenticated-orcid":false,"given":"Jos\u00e9 Manuel","family":"\u00c1lvarez-Alvarado","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76010, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1146\/annurev-pathol-052920-040318","article-title":"The immune response in multiple sclerosis","volume":"17","author":"Farez","year":"2022","journal-title":"Annu. Rev. Pathol. Mech. Dis."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Aslam, N., Khan, I.U., Bashamakh, A., Alghool, F.A., Aboulnour, M., Alsuwayan, N.M., Alturaif, R.K., Brahimi, S., Aljameel, S.S., and Al Ghamdi, K. (2022). Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities. Sensors, 22.","DOI":"10.3390\/s22207856"},{"key":"ref_3","first-page":"4587","article-title":"Esclerosis m\u00faltiple","volume":"12","author":"Margarit","year":"2019","journal-title":"Med. Accredit. Contin. Med. Educ. Program"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1007\/s00415-019-09489-5","article-title":"A focus on secondary progressive multiple sclerosis (SPMS): Challenges in diagnosis and definition","volume":"268","author":"Inojosa","year":"2021","journal-title":"J. Neurol."},{"key":"ref_5","first-page":"1","article-title":"Multiple sclerosis: Pathogenesis, symptoms, diagnoses and cell-based therapy","volume":"19","author":"Ghasemi","year":"2016","journal-title":"Cell J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tarlinton, R.E., Martynova, E., Rizvanov, A.A., Khaiboullina, S., and Verma, S. (2020). Role of viruses in the pathogenesis of multiple sclerosis. Viruses, 12.","DOI":"10.3390\/v12060643"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1080\/14737175.2021.1978843","article-title":"Role of environmental factors in multiple sclerosis","volume":"21","author":"Zarghami","year":"2021","journal-title":"Expert Rev. Neurother."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dominguez-Mozo, M.I., Perez-Perez, S., Villarrubia, N., Costa-Frossard, L., Fernandez-Velasco, J.I., Ortega-Madue\u00f1o, I., and Alvarez-Lafuente, R. (2021). Herpesvirus antibodies, vitamin d and short-chain fatty acids: Their correlation with cell subsets in multiple sclerosis patients and healthy controls. Cells, 10.","DOI":"10.3390\/cells10010119"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1177\/1352458518768433","article-title":"Comparative analysis of natalizumab versus fingolimod as second-line treatment in relapsing\u2013remitting multiple sclerosis","volume":"24","author":"Lorscheider","year":"2018","journal-title":"Mult. Scler. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1016\/j.intimp.2009.03.004","article-title":"Interferon-beta modifies the peripheral blood cell cytokine secretion in patients with multiple sclerosis","volume":"9","author":"Mirandola","year":"2009","journal-title":"Int. Immunopharmacol."},{"key":"ref_11","first-page":"149","article-title":"The molecular study of IFN-\u03b2 pleiotropic roles in MS treatment","volume":"12","author":"Kay","year":"2013","journal-title":"Iran. J. Neurol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ho\u010devar, K., Risti\u0107, S., and Peterlin, B. (2019). Pharmacogenomics of multiple sclerosis: A systematic review. Front. Neurol., 10.","DOI":"10.3389\/fneur.2019.00134"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, L., Olcer, M., Wang, Z., Song, Y., Ke, J., Feng, X., and Reder, A.T. (2025). IFN-\u03b2 therapy rescues dysregulated IFN-stimulated proteins, serum cytokines, and neurotrophic factors in multiple sclerosis: Multiplex analysis of short-term and long-term IFN responses. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0330867"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"154911","DOI":"10.1016\/j.cyto.2019.154911","article-title":"Effects of interferon and glatiramer acetate on cytokine patterns in multiple sclerosis patients","volume":"126","author":"Ganji","year":"2020","journal-title":"Cytokine"},{"key":"ref_15","first-page":"108322","article-title":"Effect of genetic polymorphisms on therapeutic response in multiple sclerosis relapsing-remitting patients treated with interferon-beta","volume":"785","year":"2015","journal-title":"Mutat. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/s40263-021-00822-z","article-title":"Interferons and multiple sclerosis: Lessons from 25 years of clinical and real-world experience with intramuscular interferon beta-1a (Avonex)","volume":"35","author":"Cohan","year":"2021","journal-title":"CNS Drugs"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e154","DOI":"10.1212\/NXI.0000000000000154","article-title":"Pharmacogenomic study in patients with multiple sclerosis: Responders and nonresponders to IFN-\u03b2","volume":"2","author":"Bustamante","year":"2015","journal-title":"Neuroimmunol. Neuroinflamm."},{"key":"ref_18","first-page":"344","article-title":"Defining the response to interferon-\u03b2 in relapsing-remitting multiple sclerosis patients","volume":"59","author":"Nos","year":"2006","journal-title":"Ann. Neurol. Off. J. Am. Neurol. Assoc. Child Neurol. Soc."},{"key":"ref_19","unstructured":"Vela, M.E.F.V., and Guti\u00e9rrez-Aguilar, R. (2020). Ciencias \u201c\u00f3micas\u201d, \u00bfc\u00f3mo ayudan a las ciencias de la salud?. Rev. Digit. Univ., 18."},{"key":"ref_20","first-page":"176","article-title":"Soft computing methods in bioinformatics: A comprehensive review","volume":"18","author":"Karlik","year":"2013","journal-title":"Math. Comput. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"114900","DOI":"10.1016\/j.eswa.2021.114900","article-title":"Expert systems: Definitions, advantages and issues in medical field applications","volume":"177","author":"Saibene","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sadoughi, F., and Arani, L.A. (2022). Intelligent computer systems for multiple sclerosis diagnosis. Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence, Academic Press.","DOI":"10.1016\/B978-0-323-90037-9.00001-1"},{"key":"ref_23","first-page":"144","article-title":"Fuzzy logic application to brain diseases diagnosis","volume":"6","author":"Ayangbekun","year":"2015","journal-title":"J. Emerg. Trends Comput. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"612","DOI":"10.25122\/jml-2020-0182","article-title":"Development of a knowledge-based clinical decision support system for multiple sclerosis diagnosis","volume":"13","author":"Hosseini","year":"2020","journal-title":"J. Med. Life"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"181","DOI":"10.31661\/jbpe.v0i0.1236","article-title":"A fuzzy expert system for early diagnosis of multiple sclerosis","volume":"12","author":"Matinfar","year":"2022","journal-title":"J. Biomed. Phys. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"72","DOI":"10.35145\/jabt.v5i2.145","article-title":"Implementation of Fuzzy Expert System to Detect Parkinson\u2019s Disease Based on Mobile","volume":"5","author":"Chen","year":"2024","journal-title":"J. Appl. Bus. Technol."},{"key":"ref_27","first-page":"205521719885983","article-title":"Machine learning in secondary progressive multiple sclerosis: An improved predictive model for short-term disability progression","volume":"5","author":"Law","year":"2019","journal-title":"Mult. Scler. J. Exp. Transl. Clin."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1038\/s41746-020-00333-z","article-title":"A short guide for medical professionals in the era of artificial intelligence","volume":"3","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cavaliere, C., Vilades, E., Alonso-Rodr\u00edguez, M.C., Rodrigo, M.J., Pablo, L.E., Miguel, J.M., L\u00f3pez-Guill\u00e9n, E., Morla, E.M.S., Boquete, L., and Garcia-Martin, E. (2019). Computer-Aided Diagnosis of Multiple Sclerosis Using a Support Vector Machine and Optical Coherence Tomography Features. Sensors, 19.","DOI":"10.3390\/s19235323"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Briganti, G., and Le Moine, O. (2020). Artificial intelligence in medicine: Today and tomorrow. Front. Med., 7.","DOI":"10.3389\/fmed.2020.00027"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1056\/NEJMra2302038","article-title":"Artificial intelligence and machine learning in clinical medicine","volume":"388","author":"Haug","year":"2023","journal-title":"N. Engl. J. Med."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Goyal, M., Khanna, D., Rana, P.S., Khaibullin, T., Martynova, E., Rizvanov, A.A., and Baranwal, M. (2019). Computational intelligence technique for prediction of multiple sclerosis based on serum cytokines. Front. Neurol., 10.","DOI":"10.3389\/fneur.2019.00781"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s40659-021-00334-6","article-title":"Identification of blood-derived candidate gene markers and a new 7-gene diagnostic model for multiple sclerosis","volume":"54","author":"Chen","year":"2021","journal-title":"Biol. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"15851","DOI":"10.1007\/s12652-021-03091-2","article-title":"MicroRNA expression classification for pediatric multiple sclerosis identification","volume":"14","author":"Casalino","year":"2023","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_35","first-page":"678","article-title":"Identification of CD4+ T cell biomarkers for predicting the response of patients with relapsing-remitting multiple sclerosis to natalizumab treatment","volume":"20","author":"Fagone","year":"2019","journal-title":"Mol. Med. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1007\/s12035-019-01792-6","article-title":"Serum exosome microRNAs predict multiple sclerosis disease activity after fingolimod treatment","volume":"57","author":"Ebrahimkhani","year":"2020","journal-title":"Mol. Neurobiol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Giordani, P., Ferraro, M.B., and Martella, F. (2020). Non-Hierarchical Clustering. An Introduction to Clustering with R, Springer.","DOI":"10.1007\/978-981-13-0553-5"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s00357-022-09419-7","article-title":"Hierarchical means clustering","volume":"39","author":"Vichi","year":"2022","journal-title":"J. Classif."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gurevich, M., Miron, G., Falb, R.Z., Magalashvili, D., Dolev, M., Stern, Y., and Achiron, A. (2015). Transcriptional response to interferon beta-1a treatment in patients with secondary progressive multiple sclerosis. BMC Neurol., 15.","DOI":"10.1186\/s12883-015-0495-x"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"110339","DOI":"10.1016\/j.clim.2024.110339","article-title":"Patients with multiple sclerosis who develop immunogenicity to interferon-beta have distinct transcriptomic and proteomic signatures prior to treatment which are associated with disease severity","volume":"267","author":"Coelewij","year":"2022","journal-title":"Clin. Immunol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1002\/pds.5297","article-title":"Classification and visualization of longitudinal patterns of medication dose: An application to interferon-beta-1a and amitriptyline in patients with multiple sclerosis","volume":"30","author":"Rousseau","year":"2021","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1080\/15592294.2021.2020436","article-title":"DNA methylation changes in glial cells of the normal-appearing white matter in Multiple Sclerosis patients","volume":"17","author":"Kular","year":"2022","journal-title":"Epigenetics"},{"key":"ref_43","first-page":"e8812","article-title":"Feature selection based on differentially correlated gene pairs reveals the mechanism of IFN-therapy for multiple sclerosis","volume":"8","author":"Tao","year":"2020","journal-title":"Bioinform. Genom."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1007\/s10489-015-0737-z","article-title":"A novel criterion to select hidden neuron numbers in improved back propagation networks for wind speed forecasting","volume":"44","author":"Madhiarasan","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yang, X.S. (2020). Nature-Inspired Optimization Algorithms, Academic Press.","DOI":"10.1016\/B978-0-12-821986-7.00013-5"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Robert, C. (2021). Statistics and Analysis of Scientific Data, Taylor & Francis.","DOI":"10.1080\/09332480.2021.1885943"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"de Leon-Sanchez, E.R.P., Mendiola-Santiba\u00f1ez, J.D., Dominguez-Ramirez, O.A., Herrera-Navarro, A.M., Vazquez-Cervantes, A., Jimenez-Hernandez, H., and Senties-Madrid, H. (2023). Fuzzy logic system for classifying multiple sclerosis patients as high, medium, or low responders to interferon-beta. Technologies, 11.","DOI":"10.3390\/technologies11040109"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Czabanski, R., Jezewski, M., and Leski, J. (2017). Introduction to fuzzy systems. Theory and Applications of Ordered Fuzzy Numbers, Springer International Publishing.","DOI":"10.1007\/978-3-319-59614-3_2"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Rutkowska, D. (2001). Neuro-Fuzzy Architectures and Hybrid Learning, Springer Science & Business Media.","DOI":"10.1007\/978-3-7908-1802-4"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Salgado-Gomes-Sagaz, F., Zorrilla-Mu\u00f1oz, V., and Garcia-Aracil, N. (2024). Rehabilitation Technologies by Integrating Exoskeletons, Aquatic Therapy, and Quantum Computing for Enhanced Patient Outcomes. Sensors, 24.","DOI":"10.3390\/s24237765"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Prokopowicz, P., Czerniak, J., Miko\u0142ajewski, D., Apiecionek, \u0141., and \u015alezak, D. (2017). Theory and Applications of Ordered Fuzzy Numbers: A Tribute to Professor Witold Kosi\u0144ski, Springer Nature.","DOI":"10.1007\/978-3-319-59614-3"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1007\/s00453-012-9717-4","article-title":"Analysis of agglomerative clustering","volume":"69","author":"Ackermann","year":"2014","journal-title":"Algorithmica"},{"key":"ref_53","unstructured":"Mirjalili, V., and Raschka, S. (2020). Python Machine Learning, Packt Publishing Ltd."},{"key":"ref_54","unstructured":"Jha, A.R., and Pillai, D.G. (2021). Mastering PyTorch, Packt Publishing."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mohammed, M., Khan, M.B., and Bashier, E.B.M. (2016). Machine Learning: Algorithms and Applications, CRC Press.","DOI":"10.1201\/9781315371658"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"104416","DOI":"10.1016\/j.compbiomed.2021.104416","article-title":"Machine learning in diagnosis and disability prediction of multiple sclerosis using optical coherence tomography","volume":"133","author":"Montolio","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine learning: Algorithms, real-world applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s10462-011-9272-4","article-title":"Decision trees: A recent overview","volume":"39","author":"Kotsiantis","year":"2013","journal-title":"Artif. Intell. Rev."},{"key":"ref_59","unstructured":"Stevens, E., Antiga, L., and Viehmann, T. (2020). Deep Learning with PyTorch, Simon and Schuster."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s40537-021-00428-8","article-title":"An alternative approach to dimension reduction for pareto distributed data: A case study","volume":"8","author":"Roccetti","year":"2021","journal-title":"J. Big Data"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Villegas-Mier, C.G., Rodriguez-Resendiz, J., \u00c1lvarez-Alvarado, J.M., Jim\u00e9nez-Hern\u00e1ndez, H., and Odry, A. (2022). Optimized random forest for solar radiation prediction using sunshine hours. Micromachines, 13.","DOI":"10.3390\/mi13091406"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Garcia-Villegas, E., Lopez-Garcia, A., and Lopez-Aguilera, E. (2023). Genetic Algorithm-Based Grouping Strategy for IEEE 802.11ah Networks. Sensors, 23.","DOI":"10.3390\/s23020862"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Vasuki, A. (2020). Nature-Inspired Optimization Algorithms, CRC Press.","DOI":"10.1201\/9780429289071"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1111\/ene.12883","article-title":"Disease-modifying treatments for multiple sclerosis\u2014A review of approved medications","volume":"23","author":"Torkildsen","year":"2016","journal-title":"Eur. J. Neurol."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/5\/523\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:28:02Z","timestamp":1777548482000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/5\/523"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,30]]},"references-count":64,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["bioengineering13050523"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13050523","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,30]]}}}