Classification of medicines using naive bayes classifier

Abstract

In data mining there is a function called classification that assigns items into groups based on their similarities. These classification techniques are used in many real time applications to classify the data. In pharmaceutical industries all the new medicines need to get approval from Food and Drug administration (FDA) before bringing them into the market. FDA is completely depended on its manual operation and leads to more amount of time for the approval of new medicines. By using classification technique the FDA can automate the procedure with which the time required for approval of new medicine can be reduced. For classification of unknown medicines six chemical properties are used such as relative molecular mass, Number of hydrogen bond donor, Number of hydrogen bond acceptor, Polar surface Area , Hydrophobic constant and number of flexible rotation keys. Drug category and these six properties are collected from U.S National Library of Medicine for creating a new database. By applying Naive Bayes algorithm on the collected data, target class of unknown medicines can be accurately predicted. The fundamental purpose of this paper is to provide a comprehensive analysis of classification methods for predicting unknown medicines. And even discusses the computation procedure of binary classification system for fever and typhoid medicines.