Machine Learning Approaches in Smart Health

• Published in: Procedia computer science Vol. 154; pp. 361 - 368
• Main Authors: Rayan, Zeina, Alfonse, Marco, Salem, Abdel-Badeeh M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2019
• Subjects: Electronic Health; Machine Learning; Medical Informatics; Smart Health; Electronic Health; Smart Health; Medical Informatics; Machine Learning
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2019.06.052

The increase of age average led to an increase in the demand of providing and improving the service of healthcare. The advancing of the information and communication technology (ICT) led to the development of smart cities which have a lot of components. One of those components is Smart Health (s-Health), which is used in improving healthcare by providing many services such as patient monitoring, early diagnosis of diseases and so on. Nowadays there are many machine learning techniques that can facilitates s-Health services. This paper reviews recent published papers in the area of smart health starting from the years 2011 to 2017, and a structured analysis for different machine learning (ML) approaches that are applied in s-Health. The results show that the ML approach is used in many s-Health applications such as Glaucoma diagnosis, Alzheimer’s disease, bacterial sepsis diagnoses, the Intensive Care Unit (ICU) readmissions, and cataract detection. The Artificial Neural Network (ANN), Support Vector Machine (SVM) algorithm and deep learning models especially the Convolutional Neural Network (CNN) are the most commonly used machine learning approaches where they proved to get high evaluation performance in most cases.