Predicting the total Unified Parkinson’s Disease Rating Scale (UPDRS) based on ML techniques and cloud-based update

نویسندگان

چکیده

Abstract Nowadays, smart health technologies are used in different life and environmental areas, such as life, healthcare, cognitive cities, social systems. Intelligent, reliable, ubiquitous healthcare systems a part of the modern developing technology that should be more seriously considered. Data collection through ways, Internet things (IoT)-assisted sensors, enables physicians to predict, prevent treat diseases. Machine Learning (ML) algorithms may lead higher accuracy medical diagnosis/prognosis based on data provided by sensors help tracking symptom significance treatment steps. In this study, we applied four ML methods Parkinson’s disease assess methods’ performance identify essential features predict total Unified Rating Scale (UPDRS). Since accessibility high-performance decision-making so vital for updating supporting IoT nodes (e.g., wearable sensors), all is stored, updated rule-based, protected cloud. Moreover, assigning computational equipment memory use, cloud computing makes it possible reduce time complexity training phase cases want create complete structure cloud/edge architecture. situation, investigate approaches with varying iterations without concern system configuration, temporal complexity, real-time performance. Analyzing coefficient determination Mean Square Error (MSE) reveals outcomes mostly at an acceptable level. algorithm’s estimated weight indicates Motor UPDRS most significant predictor Total UPDRS.

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ژورنال

عنوان ژورنال: Journal of Cloud Computing

سال: 2023

ISSN: ['2326-6538']

DOI: https://doi.org/10.1186/s13677-022-00388-1