Drug Side Effect Prediction Using Supervised Machine Learning Techniques
Keywords:
Supervised Learning, Machine Learning, Logistic Regression, Linear SVM, Random ForestAbstract
In the pharmaceutical sector, drug combinations are now a popular research issue, but experiment-based approaches are quite expensive in terms of both time and money. Many computational techniques have been put out to tackle these issues by beginning with current pharmacological combinations. To properly evaluate medication combinations, however, only chemical structure information is often provided, covering a rather narrow range of pharmacological properties. By combining the neighbour recommender approach with ensemble learning algorithms, we incorporated similarity-based multi-feature drug data to increase prediction accuracy. We chose the best medication characteristics by feature evaluation analysis, achieving an accuracy of 0.801 in the ensemble models. . Results of the study demonstrated that ensemble models outperformed conventional machine learning techniques including support vector machines (SVM), random forests (RF), and logistic regression (GLM). Additionally, we predicted 10 potential medication combinations for the medicine paclitaxel, and we were able to effectively confirm that two of the anticipated combinations have positive outcomes. The goal of this project take the input data from user and predict the side effects accurately automatic.
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