A Healthcare Recommender System Framework

Abstract

After the pandemic hit every part of the world, healthcare awareness is slowly rising among every human being, especially leaders of each country. Due to a shortage of manpower in the healthcare industry, patients tend to search the internet for some self diagnoses. This way is extremely dangerous as patients might end up using the wrong treatment such as taking the wrong medication to treat their sickness since there are so many different remedies posted on the internet without valid recognition from the healthcare professionals. To aid in overcoming this problem, this research will be building a Healthcare Recommender System. The goal of a Healthcare Recommender System (HRS) aims to supply its user with medical information that is meant to be highly relevant and tailored to an individuals need. Hence, this paper gives an overview of various recommender systems, datasets employed, and evaluation metrics used in the healthcare system. In addition, we propose the framework for the HRS to capture user input on their condition and recommend the next course of action. The steps involved in our recommender system includes choosing the dataset and techniques, data cleaning and preprocessing, building the recommender system, training the recommender engine, and finally performing the prediction. We generate the accuracy of prediction and analyze with some results. From the experimental results, Cosine Similarity has the highest accuracy compared to Jaccard Similarity and Euclidean Distance.