The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things - Aminizadeh, Sarina; Heidari, Arash; Toumaj, Shiva; Darbandi, Mehdi; Navimipour, Nima Jafari; Rezaei, Mahsa; Talebi, Samira; Azad, Poupak; Unal, Mehmet (Computer methods and programs in biomedicine, 2023)
•Providing a comprehensive analysis of the most current innovations in medical data processing.•Proposing a systematic review of the available platforms for medical data processing.•Providing an overview of the most basic ML/DL-based methodologies in medical data processing.•Presenting a summary of distributed computing methods for health-care data analysis with classifying them based on practical characteristics of the techniques.•Evaluating each method that is associated with numerous aspects such as advantages, challenges, databases, implementations, privacy, and security matters.•Outlining the vital aspects where the preceding strategies may be improved soon. Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL algorithm was the Convolutional Neural Network (CNN), with a percentage of 19.4%. Hence, despite how technology changes, delivering appropriate therapy for patients is the primary aim of healthcare-associated departments. Therefore, further studies are recommended to develop more functional architectures based on DL and distributed environments and better evaluate the present healthcare data analysis models.