Predicting Drug Responses: A Comparative Analysis and Visualization of Machine Learning Models

This study provides a practical solution for predicting the drug response by constructing deep learning algorithms and risk modelling. A suitable data set that is appropriate for building prediction models and follows a systematic approach with multiple preprocessing and feature engineering phases is identified. Hyperparameter tuning improves the accuracy of models trained with various algorithms to predict drug responses, including neural networks. A comprehensive set of model performance metrics, including accuracy, precision, recall, and Receiver Operating Characteristic - Area Under the Curve (ROC-AUC), is utilized to ensure completeness in evaluating the results. The visualization markup comprises the evaluation metrics such as confusion matrices, plots of feature importances, and other visualization figures that enable understanding and explanation of model predictions and behavior. In addition to this, some interpretable techniques are also applied to the output of the models to increase clarity and support the evaluation of predictions. This not only assures an efficient and accurate prediction framework but also helps understand the mechanisms that improve the understanding of factors affecting drug response, which is useful in the development of novel drugs and targeting specific diseases.