Recommender System for E-Health
DOI:
https://doi.org/10.56294/dm2024.370Keywords:
Health care recommender system, Machine learning, Predictive modeling, Classification method, Decision tree, Confusion metrics, Classification PerformanceAbstract
Introduction; E-healthcare management services can be significantly enhanced through the implementation of recommender systems, as highlighted in various research papers. These systems, such as Healthcare Recommender Systems (HRS) and Health Care Recommender Systems (HCRS), utilize advanced algorithms and machine learning techniques to provide personalized health recommendations based on user input and medical data.
Objective; Recommend healthcare services based on patient's state. Model healthcare information network for efficient service recommendation.
Methodology; Recommends healthcare services based on patient's critical situation and requirements. Offers re-configurable healthcare workflows to medical staff. Machine learning method classification is applied using decision tree and its result is presented which reflects 70 to 75% accuracy in predictive models which ensure that health recommender system is a full proof system.
Result; Hospital recommender systems represent a significant advancement in healthcare, providing personalized and data-driven recommendations to patients.
Conclusion; The integration of recommender systems in e-healthcare management services holds great potential in improving personalized patient care, promoting health awareness, and optimizing the quality of healthcare recommendations. In this paper author analyzed and estimated the level of accuracy of recommendation systems in healthcare for personalized medical treatment. It surveys current applications, challenges, and future directions in this field.
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Copyright (c) 2024 Ahmad Abdullah Aljabr, Kumar (Author)
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