The Review of Recent Recommendation and Classification Methods for Healthcare Domain

Nowadays, Healthcare services are dependent on Health Information Systems (HIS). The Healthcare recommendation system plays a vital role in healthcare services, work as an essential tool for decision-making tasks. Health recommendation systems improve technology accessibility and simultaneously reduce the overload of information. Although technological advancement inside the medical domain extends back years, there are still numerous difficulties to be resolved. There are various tools and recommendations for doctors in health recommendation systems (HRS). The HRS can be based on collaborative filtering, content filtering, and knowledge filtering based or may be based on hybrid filtering-based techniques. HRS is used to evaluate patient information to derive the quality of content and aid in disease diagnosis and prediction. Patients can take medicine recommendations with the help of HRS. The classification models have discussed the existing performance metrics and comparative analysis such as LSTM, Fuzzy-Logic, CNN, CNN-LSTM, etc. These classification models have improved the precised parameters as compared with the existing deep learning models. It is used to monitor the wellness and critical condition of the patients. This study is seen to be a useful starting point and the foundation for HRS literature evaluation.