{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T11:23:40Z","timestamp":1787916220401,"version":"build-2784847793"},"reference-count":53,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"CONAHCyT","doi-asserted-by":"crossref","award":["2021-000018-02NACF-12228"],"award-info":[{"award-number":["2021-000018-02NACF-12228"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study evaluates machine learning algorithms\u2019 effectiveness in classifying Parkinson\u2019s disease and Huntington\u2019s disease based on biomarker data obtained non-invasively from patients and healthy controls.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Datasets containing biomarker data ( x, y, and z values of accelerometers) from sensors were collected from Parkinson\u2019s disease, Huntington\u2019s disease patients, and healthy controls. An automatic selection model method was implemented for disease classification, using a unique Mexican database of human gait biomarkers, which we consider the only one of its kind. Random forest, random subspace method, and K-star algorithms were employed, with parameters optimized through an automated model selection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The study achieved a 0.893 precision rate for Parkinson\u2019s disease and Huntington\u2019s disease using the random subspace method. The findings underscore the potential of machine learning techniques in medical diagnosis, particularly in neurological disorders.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The automatic selection model method demonstrated efficacy in classifying Parkinson\u2019s disease and Huntington\u2019s disease based on non-invasive biomarker data. This research contributes to advancing non-invasive diagnostic approaches in neurological disorders, highlighting the significance of machine learning in healthcare.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076241284376","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T04:37:08Z","timestamp":1727411828000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Automatic selection model to identify neurodegenerative diseases"],"prefix":"10.1177","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2357-7799","authenticated-orcid":false,"given":"Eddy","family":"S\u00e1nchez-DelaCruz","sequence":"first","affiliation":[{"name":"Artificial Intelligence Laboratory, Tecnol\u00f3gico Nacional de M\u00e9xico\/Instituto Tecnol\u00f3gico Superior de Misantla, Veracruz, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4584-0076","authenticated-orcid":false,"given":"Cecilia-Irene","family":"Loeza-Mej\u00eda","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Laboratory, Tecnol\u00f3gico Nacional de M\u00e9xico\/Instituto Tecnol\u00f3gico Superior de Misantla, Veracruz, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C\u00e9sar","family":"Primero-Huerta","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Laboratory, Tecnol\u00f3gico Nacional de M\u00e9xico\/Instituto Tecnol\u00f3gico Superior de Misantla, Veracruz, Mexico"},{"name":"Divisi\u00f3n de Ingenier\u00eda en Sistemas Computacionales, Tecnol\u00f3gico Nacional de M\u00e9xico\/Valle de Bravo, Valle de Bravo Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mirta","family":"Fuentes-Ramos","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Laboratory, Tecnol\u00f3gico Nacional de M\u00e9xico\/Instituto Tecnol\u00f3gico Superior de Misantla, Veracruz, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"bibr1-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2017.06.036"},{"key":"bibr2-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1101\/cshperspect.a028035"},{"key":"bibr3-20552076241284376","first-page":"61","volume-title":"Digital health innovation for consumers, clinicians, connectivity and community","author":"Ireland D","year":"2016"},{"key":"bibr4-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(10)70245-3"},{"key":"bibr5-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjmed.2019.03.001"},{"key":"bibr6-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1016\/j.freeradbiomed.2013.01.018"},{"key":"bibr7-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp.2006.103788"},{"key":"bibr8-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1007\/s10654-011-9581-6"},{"key":"bibr9-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp.51.6.745"},{"key":"bibr10-20552076241284376","first-page":"1","volume":"9","author":"Ghosh R","year":"2018","journal-title":"Polyglutamine Disord"},{"key":"bibr11-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1002\/mds.25422"},{"key":"bibr12-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1111\/ene.13413"},{"key":"bibr13-20552076241284376","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2020.00571"},{"key":"bibr14-20552076241284376","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2021.668699"},{"key":"bibr15-20552076241284376","doi-asserted-by":"publisher","DOI":"10.2174\/0929867328999210111211420"},{"key":"bibr16-20552076241284376","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-219263"},{"key":"bibr17-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(19)30044-4"},{"key":"bibr18-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1002\/mds.26572"},{"key":"bibr19-20552076241284376","first-page":"99","volume":"29","author":"Albani G","year":"2014","journal-title":"Funct Neurol"},{"key":"bibr20-20552076241284376","doi-asserted-by":"publisher","DOI":"10.11138\/FNeur\/2017.32.1.028"},{"key":"bibr21-20552076241284376","doi-asserted-by":"publisher","DOI":"10.1007\/s10439-018-2104-9"},{"key":"bibr22-20552076241284376","doi-asserted-by":"crossref","unstructured":"Tien I, Glaser SD, Aminoff MJ. 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