  With the advancement in the technology sector spanning over every field, a
huge influx of information is inevitable. Among all the opportunities that the
advancements in the technology have brought, one of them is to propose
efficient solutions for data retrieval. This means that from an enormous pile
of data, the retrieval methods should allow the users to fetch the relevant and
recent data over time. In the field of entertainment and e-commerce,
recommender systems have been functioning to provide the aforementioned.
Employing the same systems in the medical domain could definitely prove to be
useful in variety of ways. Following this context, the goal of this paper is to
propose collaborative filtering based recommender system in the healthcare
sector to recommend remedies based on the symptoms experienced by the patients.
Furthermore, a new dataset is developed consisting of remedies concerning
various diseases to address the limited availability of the data. The proposed
recommender system accepts the prognostic markers of a patient as the input and
generates the best remedy course. With several experimental trials, the
proposed model achieved promising results in recommending the possible remedy
for given prognostic markers.
