Mapping of medicine data with k-means and apriori combinations based on patient diagnosis

Abstract

Medicine is one of the items needed by sick society, the high influence of medicine on service and the economy in hospitals, requires mapping and planning the optimal need for medicines according to the conditions, because 50% -60% of hospital income is sourced from medicine sales. The purpose of this study was to find patterns of doctor's prescription medicine association with sales data using an apriori algorithm based on data grouping using a k-means algorithm. The results of the experiments show that medicine prescription data with medicine sales have significant differences so that the data can not be used as materials for medicine planning, this is due to some indication of one of the unavailability of medicine caused by mapping inaccuracy so that the planning of medicine requirements is not optimal. The results of this analysis can be used as input materials in decision making, so the planning needs of medicines can be in accordance with the development of patient disease patterns.