A Review on Machine Learning and Deep Learning Techniques for Medical Internet of Things (m-IoT)

Abstract

The mobile health care over Internet of Things (IoT) offers flexibility and fast clinical diagnosis irrespective of distance and viewing displays. The delivery, management, and oversight of medical services have undergone a radical transformation as a result of the integration of Machine Learning (ML) and Deep Learning (DL) techniques in the mobile healthcare domain. To achieve better Quality of Service (QoS), the present networks needs more lime light of research in terms of streaming the medical videos without sacrificing the medical quality of experience (mQoE).Researchers have addressed various issues and proposed numerous solutions in terms of machine learning for IoT in mobile healthcare. The article provides a thorough analysis of contemporary machine learning (ML)/Deep Learning (DL) concepts, with a focus on mobile healthcare domain. It mainly focus on recent developments in health care domain and also opens research issues that need to be addressed in the future A wide range of topics, including the diagnosis and classification of diseases, customised therapy suggestions, remote patient surveillance and their health behaviour analysis have been covered. A comprehensive review has been conducted on IoT in healthcare, Big medical data in IoT, ML and DL models and ML challenges in IoT healthcare. This survey of IoT based medical data transmission is focused on recognising an accurate prediction model. The simulation results and comparison analysis of various ML/DL algorithms have been presented. The medical image classification have been evaluated based on several metrics including prediction accuracy, specificity and sensitivity. These metrics have been compared and tabulated for various learning algorithms like Artificial Neural Networks (ANN),Naïve Bayes Classifier (NB), Reinforcement Learning (RF),Support Vector machine (SVM),Convolutional Neural Networks (CNN). Finally, a frame work for a secured multipath medical video transmission using the hybrid version of the above discussed algorithms has also been suggested.