A Predictive IoT and Cloud Framework for Smart Healthcare Monitoring Using Integrated Deep Learning Model

Authors

Preeti Rani, Assistant Professor, College Of Computing Sciences & IT (CCSIT), Teerthanker Mahaveer University, Delhi Road, Moradabad-244001 (U.P), IndiaFollow
Umesh Chandra Garjola, Assistant Professor, College Of Computing Sciences & IT (CCSIT), Teerthanker Mahaveer University, Delhi Road, Moradabad-244001 (U.P), IndiaFollow
Haider Abbas, Computer Engineering and Information Technology Department, College of Engineering, University of Qom, IranFollow

Abstract

The researchers developed a deep learning-based smart healthcare monitoring system based on IoT and cloud technology. The proposed system integrates IoT sensors for real-time collection of physiological data, such as ECG, blood pressure, and heart rate, with cloud computing for secure storage and advanced analytics. Utilizing the Bi-LSTM model with fuzzy inference systems (FIS), the framework enhances the accuracy and efficiency of heart disease prediction. According to the evaluation, the model performs better in terms of accuracy, precision, recall, and F1 score than traditional LSTM and FLSTM models. By enabling early detection and personalized interventions, the system aims to reduce mortality rates associated with cardiovascular diseases and improve healthcare delivery through smart, data-driven solutions.

Article Type

Article

Revise Date

12-22-2024

Recommended Citation

Rani, Preeti; Garjola, Umesh Chandra; and Abbas, Haider (2024) "A Predictive IoT and Cloud Framework for Smart Healthcare Monitoring Using Integrated Deep Learning Model," NJF Intelligent Engineering Journal: Vol. 1: Iss. 1, Article 5.
Available at: https://iej.iunajafjournals.com/journal/vol1/iss1/5