Paper Title : ADVERSE MEDICINE REACTION ANALYSIS USING SUPPORT VECTOR MACHINE MODEL

ISSN : 2394-2231
Year of Publication : 2021

10.29126/23942231/IJCT-v8i2p30

Authors: C.Mani MCA., M.E., V.Madhan Kumar

         



MLA Style: C.Mani MCA., M.E., V.Madhan Kumar "ADVERSE MEDICINE REACTION ANALYSIS USING SUPPORT VECTOR MACHINE MODEL " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: C.Mani MCA., M.E., V.Madhan Kumar "ADVERSE MEDICINE REACTION ANALYSIS USING SUPPORT VECTOR MACHINE MODEL " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
Adverse Drug Reaction (ADR) is one in every of the various uncertainties that are considered a fatal threat to the pharmacy industry and therefore the field of diagnosing. Utmost care is taken to check a brand new drug thoroughly before it's introduced and made available to the general public. However, these pre-clinical trials don't seem to be enough on their own to confirm safety. The increasing concern to the ADRs has motivated the event of statistical, data processing and machine learning methods to detect the Adverse Drug Reactions. With the provision of Electronic Health Records (EHRs), it's become possible to detect ADRs with the mentioned technologies. during this work, we've proposed a hybrid model of information mining and machine learning to spot different Adverse Reactions and predict the intensity of the end result. we've used the Proportionality Reporting Ratio (PRR) together with the precision point estimator test called the Chi-Square test to search out out the various relationships between drug and symptoms called the drug-ADR association. This output from the information mining technique is employed as an input to the machine learning algorithms like Random Forest and Support Vector Machine (SVM) to predict the intensity of the end result of ADR, looking on a patient’s demographic data like gender, weight, age, etc. during this work, we've achieved an accuracy of 91% to predict ‘death’ because the outcome from an ADR.

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Keywords
— Adverse Drug Events, Healthcare, Medical Diagnosis, Data mining, Machine Learning, Random Forest, Support Vector Machine, Drug-Symptom association.