PREDICTING SAFETY INFORMATION OF DRUGS USING DATA MINING TECHNIQUE

Data Classification is the application of data mining techniques to discover patterns from the micro array and biological datasets. This research entitled “PREDICTING SAFTEY INFORMATION OF DRUGS USING DATA MINING TECHNIQUE” incorporates information theory, which is the process of deriving the information from the unsupervised dataset through feature selection. Finding the best features that are similar to a test data is challenging task in current data era. This research presents a framework for discovering best feature selection from unsupervised datasets. The proposed research work presents a new approach to measure the features (attributes) in drug prediction dataset using the methodologies namely, data cleaning, Adaptive Relevance Feature Discovery and Random Forest Classification. There are number of pharmacy companies are available in the market with multiple medicines for same problem.This prediction of drugs is used to prescribe the medicines for the patient's disease by analyzing the history of the patient's health. Feature selection and dimensionality reduction is characterized by a regularity analysis where the feature values correspond to the number times that term appears in the dataset. The relevance feature discovery method gives a useful measure is used to find the similarity features between data points are likely to be in terms of their features property. Some of the challenges faced in finding the best feature selection include positive, negative and inconsistency. This Proposed work proposes an enhanced Drug prediction based on Random Forest classification method to estimate the feature searching that is measured using minimal redundancy optimization method corresponding to drug prediction dataset