RETRACTED ARTICLE: Optimizing appointment scheduling for out patients and income analysis for hospitals using big data predictive analytics

Predictive Analytics is aided mainly by Big Data Technology tools. Predictive Analytics identifies meaningful patterns, and Big Data technologies like Hadoop and Hive help analyze and transform the results to assist in Business Intelligence. While maximization of the outpatient processes is plentiful, the help of Hadoop and Python can help support decision making. Efficiently scheduling out-patients in clinics can better utilize time and space and bring the hospital more money, resulting in better patient satisfaction. In our work, we focus on both Big Data and Predictive technologies to create an effective appointment-scheduling platform for outpatients. We use Random Forest Regressor (RFR) to design a recommendation system that helps us predict the rating of a doctor based on the waiting time in outpatient clinics. Hadoop’s MapReduce, a useful tool for processing large datasets, is used to schedule the patients to the best practitioner for treatment according to their specialization and rating. Besides, based on the assignment, it would calculate the income generated by a doctor in a hospital and perform an analysis using Hive. Finally, we compare the performance of various recommendation systems. We then compare the scheduling algorithms using different data sets varying in sizes and find that the elapsed time for mapping and scheduling patients invariably remains the same irrespective of the size of the data. Finally, an analysis of the income generated evaluated.