Identification of the choice of drugs in epilepsy by the method of classification and regression tree

Abstract

Data Mining helps its users deduce important information from huge databases. In medical stream, practitioners make use of huge patient data. Any effective medical treatmentis achieved after complete survey of ample amount of patient data. But practitioners usually faced with the obstacle of deducing pertinent information and finding certain trend or pattern that may further help them in the analysis or treatment of any disease. Data Mining is such a tool which sifts through that voluminous data and presents the data of essential nature. In this paper, we have designed a five-step data mining model that will help medical practitioners on determining the appropriate drug to be used in ministration for epilepsy. Most of the epileptic seizures are managed through drug remedy, particularly anti-convulsant drugs. The choice is most often related to other aspects particular to every patient. The trick to building a successful predictive model is to include parts of data in your database that describes what has happened in the past. There are a wide range of older as well as recent anticonvulsants present in market. Our paper will take into consideration both the older and the recent anticonvulsants and other factors to justify the use of a drug suitable for treatment in epilepsy. To determine the drug choice for treatment in different epilepsy, we have selected the classification method. Decision trees are a sort of data mining technology that has been around for almost 20 years now. They are now increasingly being used for prediction.