Drug Review System Using Machine Learning by Comparing Linear Support Vector Machine with Naïve Bayes Classifier to Measure Accuracy
P A Dhanush, N. Nalini · 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · 2022
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.