IoT-based multiclass decision support system of chronic kidney disease using optimal DNN.

Shanmugarajeshwari, V.; Ilayaraja, M.

Current healthcare applications commonly incorporate the Internet of Things (IoT) and cloud computing ideas. IoT devices provide massive amounts of patient data in the healthcare industry. These data stored in the cloud are analyzed using mobile devices' built-in storage and processing power. The Internet of Medical Healthcare Things (IoMHT) integrates health monitoring components including sensors and medical equipment to remotely monitor patient records in order to provide more intelligent and sophisticated healthcare services. In this research, we analyze one of the deadliest illnesses with a high fatality rate worldwide, the chronic kidney disease (CKD), to provide the finest healthcare services possible to users of e-health and m-health applications by presenting the IoTC services based on healthcare delivery system for the prediction and observation of CKD with its severity level. The suggested architecture gathers patient data from linked IoT devices and saves it in the cloud alongside real-time data, pertinent medical records that are collected from the UCI Machine Learning Repository, and relevant medical documents. We further use a Deep Neural Network (DNN) classifier to predict CKD and its severity. To boost the effectiveness of the DNN classifier, a Particle Swarm Optimization (PSO)-based feature selection technique is also applied. We compare the performance of the proposed model using different classification measures utilizing different classifiers. A Quick Flower Pollination Algorithm (QFPA)- (DNN)-based IoT and cloud-based CKD diagnosis model, is presented in this paper. The CKD diagnosis steps in the QFPA- DNN model involve data gathering, preparation, feature selection and classification stages.

ARTIFICIAL neural networks; MEDICAL care; DECISION support systems; PATIENT monitoring equipment; CHRONIC kidney failure; DRUG delivery systems

International Journal of Modeling, Simulation & Scientific Computing, 2024, Vol 15, Issue 4, p1

1793-9623

Academic Journal

10.1142/S1793962324410022