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Intelligent, reliable, and ubiquitous healthcare systems are a part of the modern developing technology that should be more seriously considered. Data collection through different ways, such as the Internet of things (IoT)-assisted sensors, enables physicians to predict, prevent and treat diseases. Machine Learning (ML) algorithms may lead to higher accuracy in medical diagnosis\/prognosis based on health data provided by the sensors to help physicians in tracking symptom significance and treatment steps. In this study, we applied four ML methods to the data on Parkinson\u2019s disease to assess the methods\u2019 performance and identify the essential features that may be used to predict the total Unified Parkinson\u2019s disease Rating Scale (UPDRS). Since accessibility and high-performance decision-making are so vital for updating physicians and supporting IoT nodes (e.g., wearable sensors), all the data is stored, updated as rule-based, and protected in the cloud. Moreover, by assigning more computational equipment and memory in use, cloud computing makes it possible to reduce the time complexity of the training phase of ML algorithms in the cases we want to create a complete structure of cloud\/edge architecture. In this situation, it is possible to investigate the approaches with varying iterations without concern for system configuration, temporal complexity, and real-time performance. Analyzing the coefficient of determination and Mean Square Error (MSE) reveals that the outcomes of the applied methods are mostly at an acceptable performance level. Moreover, the algorithm\u2019s estimated weight indicates that Motor UPDRS is the most significant predictor of Total UPDRS.<\/jats:p>","DOI":"10.1186\/s13677-022-00388-1","type":"journal-article","created":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T15:02:19Z","timestamp":1674313339000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Predicting the total Unified Parkinson\u2019s Disease Rating Scale (UPDRS) based on ML techniques and cloud-based update"],"prefix":"10.1186","volume":"12","author":[{"given":"Sahand","family":"Hamzehei","sequence":"first","affiliation":[]},{"given":"Omid","family":"Akbarzadeh","sequence":"additional","affiliation":[]},{"given":"Hani","family":"Attar","sequence":"additional","affiliation":[]},{"given":"Khosro","family":"Rezaee","sequence":"additional","affiliation":[]},{"given":"Nazanin","family":"Fasihihour","sequence":"additional","affiliation":[]},{"given":"Mohammad R.","family":"Khosravi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,21]]},"reference":[{"key":"388_CR1","doi-asserted-by":"publisher","first-page":"27","DOI":"10.2528\/PIERC20112307","volume":"109","author":"S Keshavarz","year":"2021","unstructured":"Keshavarz S, Keshavarz R, Abdipour A (2021) Compact active duplexer based on CSRR and interdigital loaded microstrip coupled lines for LTE application. 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