Drug Classification Analysis Using Different MachineLearning Algorithms

Healthcare industry managers have placed a significant emphasis on ensuring the quality and financial efficiency of medications. With the advancement of technology and related techniques, there is now an opportunity to improve drug classification. Machine learning, utilizing large databases, has become a vital tool in the discovery of drug and design process. In this research, the data obtained from Kaggle was subjected to a range of machine learning algorithms, including Logistic Regression, -Nearest Neighbors, Random Forest, Gradient Boosting, Decision Tree, Adaptive Boosting, Naive Bayes, both linear and non-linear Support Vector Machine (SVM), and Bagging. The goal was to predict the most suitable drug type for patients and evaluate the accuracy of each algorithm. From the results it is observed that among all the algorithm, support vector machine outperformed with the accuracy of 98.66%.