Improvement of the Prediction of Drugs Demand Using Spatial Data Mining Tools

Abstract

The continued availability of products at any store is the major issue in order to provide good customer service. If the store is a drugstore this matter reaches a greater importance, as out of stock of a drug when there is high demand causes problems and tensions in the healthcare system. There are numerous studies of the impact this issue has on patients. The lack of any drug in a pharmacy in certain seasons is very common, especially when some external factors proliferate favoring the occurrence of certain diseases. This study focuses on a particular drug consumed in the city of Jaen, southern Andalucia, Spain. Our goal is to determine in advance the Salbutamol demand. Advanced data mining techniques have been used with spatial variables. These last have a key role to generate an effective model. In this research we have used the attributes that are associated with Salbutamol demand and it has been generated a very accurate prediction model of 5.78% of mean absolute error. This is a very encouraging data considering that the consumption of this drug in Jaen varies 500% from one period to another.