Abstract :
Recommendation systems usage is predominantly found in recommending online products and services to end consumers. It does the job of information filtering from a large pool of information available to provide a better vision in assisting decision-making. In recent times recommendation systems are making their strong presence in serious decision-making domains like health care which needs to adhere to minimal or zero error tolerance. Recent advancement in recommendation techniques, methods to address complex data and techniques to adapt context-awareness has improved the effectiveness of recommendation system. Health care experts use their past experiences in recommending high-quality medical treatment to patients. Hospitals maintain the collection of patient's information history consisting of clinical trial data and their health data in records. It helps experts to analyze them for decision making. There is a need for strategizing health care programs using digital techniques to make use of massive medical information resources available to provide scientific decision-making for precision diagnostics, treatment processes, preventive and rehabilitation programs to bring more fruitful results in combating health scares. This paper addresses the mapping of the complex health care data set into recommendation system terminology. The paper also discusses techniques to model diagnosis and treatment process recommendation system as a tool to assist medical practitioners to accurately diagnose a disease and recommend with precise treatment process for continual treatment care programs like cardio care, maternal care, TB care, and even for the COVID-19 treatment process.
