Analysis of Novel Machine learning algorithms for improved services in smart health care applications

Abstract

Artificial intelligence (AI) and machine learning (ML) will be increasingly used in healthcare because to the increasing complexity and volume of data in the industry. Payers, providers, and organizations in the life sciences are already using various forms of AI and ML. Diagnosis and treatment suggestions, patient participation and adherence, and management tasks are the main types of applications. These health monitors can keep tabs on a person's emotional and physical well-being. Numerous medical and psychological conditions can be traced back to stress, anxiety, and high blood pressure. Conditions associated with advancing age demand special consideration here, including stress, anxiety, and hypertension. Early detection of health issues through monitoring of stress, worry, and blood pressure is key to avoiding irreversible harm. This will improve people's lives by decreasing stress and medical expenses. Using covert wearable sensors & machine learning methods, develop innovative technological solutions for continuous monitoring of stress, worry, and blood pressure. This study proposes the development of an intelligent healthcare system that makes use of AI and ML in order to effectively address problems in the healthcare sector and to facilitate the optimization of care plans for individual patients. The suggested AI & ML -assisted method shows its ability to aid a patient admitted to the hospital via emergency medical services by quickly processing the patient's data and providing opportunities for early diagnosis of life-threatening illnesses. It can analyze comprehensive human genomic data & genetics in the clinic, automatically recognize complex patterns collected from radiologists, and provide radiologist reports, laboratory reports, and a plethora of other decision-support tools to aid clinicians.