EMPOWERING HEALTHCARE WITH SPONTANEOUS INTELLIGENCE: IOT INTEGRATION FOR PATIENT CONDITION MONITORING THROUGH ANDROID PLATFORM

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.