International Journal of Engineering and Technology

Patient’s adverse drug reacting reporting is a brand new idea in Pharmacovigilance which make contributions to the enrichment of current drug protection performs. In this research work focuses on implementation of machine learning techniques for patient awareness on opposing drug reaction reporting system in Chennai. The current study was a cross-sectional study which was showed for a period of one year amongst patients hospitalized at Chennai. Sample size taken was 1000 and the sample size was collected by google forms. Data was collected using a standardized questionnaire. Data entered in MS Excel and analyzed using Weka 3.8.3 and results interpreted. The NaïveBayes classifier has 93.21% accuracy level and it has take time to build the model 0.01 seconds. The SMO(Support Vector Machine) has produced the 96.75% accuracy and it has take time to build the model 0.51 seconds. The IBK machine learning algorithm has 95.28% accuracy and it has take time to build the model 0.00 second. The remaining machine learning algorithms namely ClassificationViaRegression, DecisionTable and J48 classifiers have same accuracy level like 97.34%. But the Classification Via Regression has taken the time to shape the model 1.02 seconds, DecisionTable has taken the time to shape the model 0.23 seconds, J48 classifier has taken the time to build the model 0.09 seconds. The review of consciousness between patients designates low consciousness and it could be upgraded by presenting educational interventional programs.