Drug Review System Using Machine Learning by Comparing Linear Support Vector Machine with Naïve Bayes Classifier to Measure Accuracy

In comparison to Naive Bayes (NB) Classifier, innovative Linear Support Vector Machine (LSVM) is used to forecast enhanced drug review systems for boosting Accuracy. Materials and Methods: In this research, two groups are compared, novel Linear Support Vector Machine (N = 10),Naive Bayes (N = 10) was generated according to total sample size using the g power software by taking into account alpha of 0.05, enrollment ratio of 0.1, 95% confidence interval, and power of 80%. Result: The accuracy rate of Linear Support Vector Machine (SVM) is 97.75 % whereas results of (NB) accuracy rate are 89.34%. There is a significant difference in accuracy rate (P = 0.045) with pre-test power of 80 % in SPSS Statistical analysis. Conclusion: The Linear Support Vector Machine (SVM) performs significantly better in terms of accuracy for predicting the enhanced drug review system when compared to the (NB) Classifier.