I. Introduction
Because of the exponential growth of data available on the Internet, the recommendation process has become a crucial component in the healthcare recommendation process [1]. Personalization is thus an essential factor in enabling a better user experience for undergoing detailed analysis of patients. This is quiet among any decision-making processes based on data analyzed as analyzed with basic and other hybrid schemes [2]. Deep Learning (DL) has made significant progress in recent years across various applications, including speech recognition and computer vision and hence it is multi-disciplinary [3], [4]. The recommendation is a subclass of information filtering systems that seeks to predict the ‘rating’ or ‘preference’ that a user provides based on his personal preference. There exist numerous ways of recommendation filtering schemes to build appropriate recommendation system. This paper illustrates a comprehensive study on developing a recommendation process that uses a collaborative filtering approach, which finds similarities between ratings, purchase patterns, and association rule mining framework [5].