Class Imbalance Handling with Deep Learning Enabled IoT Healthcare Diagnosis Model

Abstract

The rapid advancements in the field of big data, wearables, Internet of Things (IoT), connected devices, and cloud environment find useful to improve the quality of healthcare services. Medical data classification using the data collected by the wearables and IoT devices can be used to determine the presence or absence of disease. The recently developed deep learning (DL) models can be used for several processes such as classification, natural language processing, etc. This study presents a bacterial foraging optimization (BFO) based convolutional neural network-gated recurrent unit (CNN-GRU) with class imbalance handling (CIH) model, named BFO-CNN-GRU-CIH for medical data classification in IoT enabled cloud environment. The proposed BFO-CNN-GRU-CIH model initially enables the IoT devices to gather healthcare data and preprocess it for further processing. In addition, Lempel Ziv Markov chain Algorithm (LZMA) is employed for the compression of healthcare data to reduce the amount of data being communicated. Besides, Synthetic Minority Over-sampling Technique (SMOTE) is applied to handle class imbalance data problems. Moreover, BFO with CNN-GRU model is utilized to perform the classification process in which the hyperparameters of the CNN-GRU model are optimally adjusted by the use of BFO algorithm. In order to showcase the better performance of the BFO-CNN-GRU-CIH model, a wide range of simulations take place on three benchmark datasets and the results portrayed the betterment of the BFO-CNN-GRU-CIH model over the recent state of art approaches.