Such diseases appear after a while, where they begin to slowly evolve and long term. These diseases appear from non-healthy lagging behaviors such as malignancy, physical idle, or overlooking specific materials for a long time. Some are classified as addictive symptoms such as smoking and others because of the age of patient, such as Alzheimer, diabetes, and heart disease. Such diseases need a special health pattern and exercise a specific healthy behavior with continuous monitoring.
Published in Chapter:
Machine Learning in Healthcare: Theory, Applications, and Future Trends
Lana I. S. Hamad (Yildiz Technical University, Turkey), Elmustafa Sayed Ali Ahmed (Red Sea University, Sudan), and
Rashid A. Saeed (Taif University, Saudi Arabia)
Copyright: © 2022
|Pages: 38
DOI: 10.4018/978-1-6684-2304-2.ch001
Abstract
Due to the increase in healthcare data provided by IoT, there is a need to use new methods for data analysis. Machine learning (ML) techniques promise solutions for many challenges facing the IoT-based healthcare services. MLs provide significant improvement in different IoT aspects related to storage size, computational power, and data transfer speeds. In addition, MLs provide a number of solutions for medical imaging, resources, medical data processing, detection, diagnosis, and prediction. Recently, many applications have appeared in the field of medicine and healthcare, which are closely related to the IoT. This chapter presents basic concepts related to the use of ML techniques, in addition to some algorithms used in the medical field and healthcare technology based on IoT devices and systems. Moreover, the chapter will discuss the ML opportunities and challenges in healthcare and future trends as well. The chapter gives the reader full perception of the possibility of using ML techniques in the medical and healthcare fields, with a systematic description of their applications.