IoT-Enabled Early Detection of Diabetes Diseases Using Deep Learning and Dimensionality Reduction Techniques

Abstract

Chronic diseases, such as diabetes, cause serious challenges worldwide due to their long-lasting effect on health and quality of life. Diabetes, considered a high glucose level, requires continuous management to avoid complications like kidney failure, low vision, and heart disease. Leveraging state-of-the-art technology, especially IoT devices, shows great potential for optimizing the recognition and management of diabetes. These devices, including smart glucose monitors and wearable sensors, provide real-time data on crucial health metrics, enabling early intervention and proactive monitoring. Furthermore, incorporating deep learning (DL) techniques improves data analysis and recognizes risk factors and subtle patterns related to diabetes. By integrating IoT technology with DL techniques, the healthcare system can empower patients with tools for self-management and develop more conventional approaches for earlier diagnosis and personalized treatment plans, ultimately improving long-term health outcomes and reducing the burden of diabetes. This study develops a new IoT-enabled Driven Early Detection of Chronic Disease using the DL and Dimensionality Reduction (EDCD-DLDR) approach. The EDCD-DLDR method aims to enable IoT devices to collect patient medical data and employ the DL model for earlier diagnosis of diabetes. In the EDCD-DLDR technique, the IoT-based data acquisition process is initially involved, and the collected data gets normalized using Z-score normalization. The EDCD-DLDR technique uses an artificial rabbit optimizer-based feature selection (ARO-FS) approach for dimensionality reduction. In addition, the detection of diabetes is achieved by the attention bidirectional gated recurrent unit (ABiGRU) model. The pelican optimization algorithm (POA) based hyperparameter selection model is included to improve the performance of the ABiGRU network. A comprehensive set of simulations is made to highlight the performance of the EDCD-DLDR method on the Kaggle dataset. The experimental validation of the EDCD-DLDR method underscored a superior accuracy value of 97.14% over existing techniques.

Keywords

• Internet of Things
• chronic disease
• deep learning
• pelican optimization algorithm
• machine learning