E-Health And M-Health And E-Health Tools And Services Using Ai And Computational Intelligence

Abstract

With the development of society and the improvement of human health, chronic diseases have become the major killers. Along with the health-care business, heart disease, stroke, chronic obstructive pulmonary disease, and respiratory diseases remain the leading killers for a decade in both developing and developing countries. Synthetic intelligence (AI) has emerged as  a  promising generation in healthcare with many applications, such as analysis, treatment,  prevention and billing systems in clinics. Nowadays, a wide variety of state-of-the-art technology has emerged to aid the use of ai in health care, including large numbers and mobile network networks. As a result, digital (e) -healthcare and scientific (m) -healthcare aims to obtain records, process them, and provide a well-defined outcome to the donor (medical doctor, affected person, or caregiver). Cell phone (m-health) is a term for health monitoring using cell phones and patient monitoring devices etc. It is often regarded as one of the greatest technological advances in modern times. More recently, artificial intelligence (AI) and big data analysis within m-health have been used to provide an effective health care system. This is an important reason for the variety of informal and informal information due to the emergence of mobile applications and health care systems. In this paper, systematic reviews are performed on the use of AI and big    data analytics to improve the m-health system. Various algorithms based on AI and big data frameworks in terms of data source, strategies  used and  application area are discussed. This  paper explores the use of AI and big data analytics to give users insight and enable them to plan, using resources especially specific m-health challenges, and proposes an AI-based model and m-health big data analytics. The discovery of this paper will guide strategic planning using a combination of AI and big data as a source of effective m-health data management. The  evaluation of digital measures for therapeutic purposes depends on the capabilities of the  wearable technology to collect data and extract relevant patient management information. Artificial intelligence (AI) techniques allow for the processing of real-time information  and further learning from data-building data. We have developed a system that can detect clinical feeling from digital initiatives based on native smartphone sensors and advanced machine  learning and signal processing techniques to detect suicide risk.