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How to correctly refine and classify telemedicine sensor data is the research focus in related fields. Therefore, a detailed classification mathematical model simulation of telemedicine sensor data based on multi feature fusion is proposed. On the basis of telemedicine sensor data acquisition, it is preprocessed to reduce the computational overhead of detailed classification. The reliability features of the preprocessed telemedicine sensing data are extracted, the extracted features are fused by the principal component analysis method, and the refined classification model of telemedicine sensing data is constructed based on the principle of machine learning. The fused features are input into the model to complete the refined classification of telemedicine sensing data. The experimental results show that the correct refinement classification rate of the proposed method is more than 90%, the refinement classification accuracy is higher than 98.5%, the convergence speed is good, and the refinement classification time is 4\u2009~\u200912\u00a0s, which proves that the correct refinement classification rate and accuracy of the proposed method are high, the classification time is short, and has good application performance.<\/jats:p>","DOI":"10.1007\/s11036-022-02025-2","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T14:03:41Z","timestamp":1661177021000},"page":"1997-2006","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mathematical Model Simulation of Detailed Classification of Telemedicine Sensing Data"],"prefix":"10.1007","volume":"28","author":[{"given":"Haiying","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcin","family":"Wo\u017aniak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,22]]},"reference":[{"key":"2025_CR1","first-page":"7","volume":"7","author":"A Yan","year":"2020","unstructured":"Yan A, Zou Y, Mirchandani DA (2020) How hospitals in mainland China responded to the outbreak of COVID-19 using information technology\u2013enabled services: An analysis of hospital news webpages. 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Haiying Chen provided the algorithm and experimental results, wrote the manuscript, Marcin Wo\u017aniak revised the paper, supervised and analyzed the experiment. We also declare that data availability and ethics approval is not applicable in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}}]}}