An optimal Bi-gated recurrent unit and ResNET using IoT for healthcare monitoring.

Chronic Kidney Disease (CKD) and liver diseases are progressive diseases with high morbidity and large fatal rates occurring generally in the adult population exclusively those people affected with hypertension and diabetes. Thus this paper develops a deep learning method named snake optimization-based BiGRU model for predicting kidney and liver disease earlier and precisely. Gathered medical data of the patients using the various IoT sensor devices stored under the patient log in the cloud. These data are abundant in quantity. The pre-processing is initially carried out to refill the missing details of the patient. The Convolutional Neural Network ResNet and snake optimization (SO) are included for extracting features and selecting optimal features. The disease prediction is performed using the BiGRU neural network which learns data from two various sources such as forward and backward for providing extremely precise prediction output. For evaluating the performance we compared the recall, MCC, accuracy, specificity, and Kappa score values of the method with the four existing disease prediction methods of LDA-GA-SVM, CNN-RF, Decision Tree, and XGBoost. The proposed method achieved a higher accuracy of 98.9 %, recall of 98.9 %, sensitivity of 98.9 %, and an F1-score of 98.2 %. [ABSTRACT FROM AUTHOR]