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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      1 Jan 2024, Volume 39 Issue 1   
    Article

    EMPOWERING HEALTHCARE WITH SPONTANEOUS INTELLIGENCE: IOT INTEGRATION FOR PATIENT CONDITION MONITORING THROUGH ANDROID PLATFORM
    Vankani Manish Jitendrabhai, Dr. Rajendra Singh Kushwah
    Journal of Data Acquisition and Processing, 2024, 39 (1): 1142-1149 . 

    Abstract

    In the realm of healthcare, leveraging intelligent systems has become imperative for effective patient monitoring and management. This paper explores the integration of Internet of Things (IoT) technology with spontaneous intelligence to facilitate patient condition monitoring through the Android platform. The study focuses on comparing the performance of different machine learning models in accurately predicting patient conditions. Specifically, Support Vector Machines (SVM), Long Short-Term Memory Networks (LSTMs), and a novel combination of Online Gradient Descent and Online Random Forest (OGD + RF) are evaluated. Synthetic data with increased features is utilized to observe the models' behavior over iterations. Results indicate varying performances among the models, with SVM demonstrating stability, LSTM showing sensitivity to feature complexity, and OGD + RF exhibiting adaptability. Insights gleaned from this study inform the selection of suitable models for patient condition monitoring tasks, contributing to the advancement of healthcare systems.

    Keyword

    Healthcare, Internet of Things (IoT), Patient Monitoring, Machine Learning, OGD+RF, LSTM and SVM


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ISSN 1004-9037

         

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