A NOVEL MACHINE LEARNING FRAME WORK FOR IMPROVING THE EFFICIENCY OF HEALTH SYSTEMS USING SOFTWARE ENGINEERING TECHNIQUE

Abstract

Now a day’s technological aspect towards medical field is more for better diagnosis and to guide for good health systems. Mining available medical data to give solutions for the upcoming using machine learning became popular. The combination approaches of machine learning with other technologies are giving better results for health systems. In this paper we proposed A Novel Machine Learning Frame Work for Improving the Efficiency of Health Systems using Software Engineering Technique. Our approach uses combinational domains like software, machine learning, machine learning algorithms and available health informatics data for proper implementation of system. We have experimented on various data sets and concluded the function objects to be performed. Our work will be carried out on various phases as designing, implementing, maintaining and defining workflows, structuring information, ensuring security and privacy, performance testing and evaluation and releasing the software applications. Machine Learning(ML) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care.