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

Vivek Kshirsagar, Anil Karwankar, Meghana Nagori, Kailas Elekar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of 1st International Conference on Information and Communication Technology for Intelligent Systems
EditorsSwagatam Das, Suresh Chandra Satapathy
PublisherSpringer Science and Business Media Deutschland GmbH
Pages339-346
Number of pages8
ISBN (Print)9783319309262
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event1st International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2015 - Ahmedabad, India
Duration: 28 Nov 201529 Nov 2015

Publication series

NameSmart Innovation, Systems and Technologies
Volume51
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference1st International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2015
Country/TerritoryIndia
CityAhmedabad
Period28/11/1529/11/15

Keywords

  • Data mining
  • Decision tree classification
  • Drug choice
  • Epilepsy

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