AI-Driven Deep Learning Models for Scalable Cloud IoT Healthcare

• Author(s): Mohammad Serajuddin ; Prabhdeep Singh
• Paper ID: 1707328
• Page: 413-424
• Published Date: 14-04-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 10 April-2025

Abstract

Diabetes is one of them. Hyperglycemia, that is an increase in blood glucose levels above the range of cholesterol,?is a typical complication of diabetes, which is a chronic disorder that can adversely affect health. Existing continuous glucose monitoring (CGM) devices are set to warn users?with type 1 diabetes once their blood glucose levels reach a certain point. This can lead to the body of the patient operating at critical level until the medicine arrives to lower the?blood sugar level. This increases the chances of severe health?issues if the medicine is not taken within a particular time slot. This study proposes a novel?method that exploits recent advances in software and hardware techniques to overcome the latter. More specifically, for glucose level prediction over the course of 30 minutes,?a deep learning (DL) method of artificial intelligence is suggested. The insight from the ubiquitous CGM model will be integrated with the prediction model to predict future glucose?levels for the patient. The prediction model is also being implemented with cloud computing,?and Internet of Things systems are also being used. The CNN-RBM DL model combined with two or more RBM networks is called?cascaded CNN-RBM DL model and one of several deep learning approaches used in SoTA due to the desired attributes, such as increased prediction accuracy. As Experimental Results show, the Methodology proposed, Cloud&DL based portable, outperforms the current blood- glucose prediction methods present?in the state of the art (SoTA) by 15.589 in average accuracy express as RMSE.

Keywords

Diabetes, Deep Learning, Cloud Computing, Artificial Intelligence, Internet of Things, and Blood Glucose level prediction.

Citations

IRE Journals:
Mohammad Serajuddin , Prabhdeep Singh "AI-Driven Deep Learning Models for Scalable Cloud IoT Healthcare" Iconic Research And Engineering Journals Volume 8 Issue 10 2025 Page 413-424

IEEE:
Mohammad Serajuddin , Prabhdeep Singh "AI-Driven Deep Learning Models for Scalable Cloud IoT Healthcare" Iconic Research And Engineering Journals, 8(10)