Big data analysis on medical field for drug recommendation using apriori algorithm and deep learning

Drug recommendation in the medical field is a challenging real-world task to choose the best medications to give patients based on their medical histories and symptoms. In this designed model the primary intention of this model is to recommend the Drugs for patients. Drug review data are collected as a dataset. These collected data are pre-processed using the missing value replacement method to increase the dataset's quality. The apriori algorithm, an association rule-based classification method used to associate the received drug data according to the user's rating and review, is then used to organize the pre-processed data. These categorized or associated data are trained using the Bi-LSTM algorithm for recommending the best drug to the user based on the condition of the user. Precision, specificity, Accuracy, F1_score and recall, are some of the performance metrics for this designed model. The attained performance metrics values for the proposed model are 97, 97, 98, 98 and 97. Comparisons are made between the evaluated values and those produced using various existing approaches in which proposed showed better results. Therefore, the existing methodology for big data analysis in the medical sector for drug recommendation is less successful than the Apriori algorithm and deep learning.