Efficient Online Medical Store Finding and Availability of Medicines Using SVM Compared over CNN with Improved Accuracy

The goal of this research is to compare the performance of SVM and CNN at locating pharmacies and medicines sold online. The datasets have been extracted from internet collections, which are widely employed by medical retailers to build datasets for diagnostic testing. The dataset used for this experimental research consists of 501 records. Using a 95% confidence interval, 0.8 G-power, 0.05 alpha, and 0.2 beta quality, we were able to predict with a high degree of accuracy the efficient discovery and availability of pharmaceuticals from online medical shops. Our sample size was 20 (Group 1 = 10 and Group 2 = 10). The Support Vector Machine (SVM) has an 82.36% greater accuracy rate than the Convolutional Neural Network (CNN) classifier, which has an accuracy rating of 79.49%. The significance level of the study is p<0.05, or p=0.0363. CNN outperforms SVM in terms of accuracy rate when forecasting the efficient online medical store searching and availability of medications.