{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T14:45:43Z","timestamp":1791557143714,"version":"4.3.4"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T00:00:00Z","timestamp":1683763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Diabetic peripheral neuropathy (DN) is a serious complication of diabetes mellitus (DM) that can lead to foot ulceration and eventual amputation if not treated properly. Therefore, detecting DN early is important. This study presents an approach for diagnosing various stages of the progression of DM in lower extremities using machine learning to classify individuals with prediabetes (PD; n = 19), diabetes without (D; n = 62), and diabetes with peripheral neuropathy (DN; n = 29) based on dynamic pressure distribution collected using pressure-measuring insoles. Dynamic plantar pressure measurements were recorded bilaterally (60 Hz) for several steps during the support phase of walking while participants walked at self-selected speeds over a straight path. Pressure data were grouped and divided into three plantar regions: rearfoot, midfoot, and forefoot. For each region, peak plantar pressure, peak pressure gradient, and pressure\u2013time integral were calculated. A variety of supervised machine learning algorithms were used to assess the performance of models trained using different combinations of pressure and non-pressure features to predict diagnoses. The effects of choosing various subsets of these features on the model\u2019s accuracy were also considered. The best performing models produced accuracies between 94\u2013100%, showing the proposed approach can be used to augment current diagnostic methods.<\/jats:p>","DOI":"10.3390\/s23104658","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T05:05:02Z","timestamp":1683781502000},"page":"4658","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Prediction of Diabetes Mellitus Progression Using Supervised Machine Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Apoorva S.","family":"Chauhan","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, University of Nevada, Las Vegas, NV 89154, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathew S.","family":"Varre","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7171-3073","authenticated-orcid":false,"given":"Kenneth","family":"Izuora","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, University of Nevada, Las Vegas, NV 89154, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3090-9672","authenticated-orcid":false,"given":"Mohamed B.","family":"Trabia","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Nevada, Las Vegas, NV 89154, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6547-4931","authenticated-orcid":false,"given":"Janet S.","family":"Dufek","sequence":"additional","affiliation":[{"name":"Department of Kinesiology and Nutrition Sciences, University of Nevada, Las Vegas, NV 89154, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,11]]},"reference":[{"key":"ref_1","unstructured":"American Diabetes Association (2022, August 23). 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