Published Paper Details:

Systems for recommending healthcare have drawn a lot of interest recently due to their potential to enhance patient outcomes and treatment. By analyzing pertinent studies, approaches, and findings, this review of the literature aims to analyze the current condition of patient health care referral systems. In the area of patient-based health recommendation systems, the study emphasizes major research areas, obstacles, and potential approaches for the future. These days, healthcare administration is highly sought after because it is so beneficial to running a hospital or a doctor's office. The globe is being impacted by the expansion of health management systems on a daily basis. The increasing demand for healthcare is caused by a number of factors, including healthcare solutions. The health prediction system is a project for online user assistance and consulting. In this study, we suggest a solution that enables users to get prompt online health advice from a smart health system. The system contains numerous ailments and symptoms linked to various systems. Using data mining tools, the most likely disease linked to a patient's symptoms can be identified. A doctor can access his patient's information and report in the doctor's module by logging into the system. Doctors can examine the patient's search history and the information the patient is seeking for based on their prognosis. His data is available to the doctor. The database's administrator can add new disease details, including the disease's kind and symptoms. Based on the name and symptoms of the condition, the data mining system operates. The database of ailments and symptoms is accessible to the administrator. In a variety of fields of application, such as the healthcare recommendation system (HRS), recommender systems use various machine learning (ML) approaches to user suggestion and recommend services or entities. The HRS and many application areas are now using ML techniques from the field of artificial intelligence because to the enormous number of algorithms shown in the literature. However, it appears to take a lot of time to choose a suitable ML algorithm for a health recommender system.