DEEP LEARNING PREDICTION OF ADVERSE DRUG REACTION ANALYSIS USING ARTIFICIAL NEURAL NETWORK MODEL

In medical domain, Adverse Drug Reaction (ADR) analysis is a crucial process for doctors and medical scientists.Adverse drug reaction measures the injury occurred due to usage of a drug. The growing concern to the ADRs hasstimulated the progress of statistical, data mining methods to find the Adverse Drug Reactions. This project proposed ahybrid model of data mining and machine learning to classify different Adverse Reactions and foretell the outcomeintensity. It used the Proportionality Reporting Ratio (PRR) along with ChiSquare test equations to find out the different relationships between drug and symptoms called the drugADR association. In addition, support vector machine method is applied to classify the data set records into either normal or adverse drug. Moreover, our project aims in finding the percent of adversity of the drug reaction. Based on the number of occurrences, where a specific drug P, causes a specific Adverse Reaction, R, the various terms are measured for PRR and Chi Square. In addition KNN andSVM classification is made on drug records to classify them based on data set columns. Neural network based classification is the proposed system to classify drug reaction based on data set columns.