Novel Internet of Things Based Disease Diagnosis Framework for Smart Healthcare Schemes using Combined Optimized Artificial Intelligence Approach
Year: 2023
Type: article
Abstract: The Internet of Things (IoT) and Artificial Intelligence (AI) technologies have gotten a lot of attention in recent years as a means to alleviate the load on healthcare systems caused by an aging population and a surge in severe diseases. However, the present diagnosis systems have several flaws, such as a long computational time and reduced prediction accuracy. Due to this reason, this article proposes a new improved IoT-based hybrid AI model for diagnosing heart, diabetes, and kidney diseases. Here, the UCI Repository dataset is used in this research. The implementation of this work is executed in MATLAB software. Also, the exact features from the dataset for accurate classification are obtained by the proposed Hierarchy mapping-based heap optimizer (HM-HO) method. Furthermore, the Shamble Shepherd Optimizer-based Evolving fuzzy neural network (SSO-EuFNN) algorithm is proposed for accurate disease diagnosis classification. Furthermore, 99.91% precision for heart disease, 99.88% accuracy for diabetes, and 99.82% accuracy for kidney disease were attained in the indicated disease diagnostic simulation findings. Thus the simulation results achieved from the proposed model are compared with the conventional methods in terms of various performance measures. As a result, the proposed strategies successfully reduce the death rate by lowering the complexity of disease diagnosis.
Institution: ICFAI Foundation for Higher Education
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7217090592