Behaviour Analysis Using Machine Learning Algorithms In Health Care Sector

A behavioral analytics approach uses big data analytics in combination with machine learning (ML) to identify patterns, trends, aberrations, and other useful insights. The behavior of an individual can be analyzed by expressions, postures, and activity levels. Using ML algorithms could revolutionize the way clinicians make decisions in health care sector. Studies of human behavior have been conducted in a range of scientific disciplines (e.g sociology, psychology, computer science). ML algorithms have the potential to transform the way doctors and instructors make choices. This methodology has been slow to be adopted by behavior analysis experts to maximize its application to practical issues and to aid them in learning more about human behavior. ML algorithms are dominating the healthcare industry. Recent researches have indicated that these techniques can be used to anticipate disease based on health data. Our study examines several machine learning algorithms used in early disease detection and identifies key trends in their performance. The analysis suggests that human behavior may play a role in a variety of conditions, including diabetes, cancer, heart disease, autism, mental illness, Alzheimer’s, and others. A number of daily habits are associated with this behavior, including food, respiration rate, blood pressure, voice output, social abnormalities, insomnia, and so on. A few examples of ML applications integrated into healthcare services are naive bayes (NB), support vector machines (SVM), random forest (RF), and convolutional neural networks (CNN). In a variety of cancer classification applications, these models are proved to be highly efficient in diagnosing various cancer types. This review includes a number of research investigations that employ ML to analyze behavioral data. As we gain further insights into the factors influencing organisms’ behavior, we are able to create computational models which allow disease prediction and management to become more accurate.