Anti Cancer Drug Response Prediction with Machine Learning and Data Driven Approaches

Abstract

This research proposes employing sophisticated machine learning approaches to improve feature selection, model performance, and guess accuracy to predict cancer treatment outcomes. Ensemble learning, SVM models, decision trees, and bootstrapped examples improve accuracy and resilience. Reduced impurity metrics discover significant features, lowering dimensions and making models simpler to interpret in huge datasets. The recommended solution outperforms others with 0.85 accuracy, 0.83 precision, 0.80 memory, and 0.81 F1 score. The AUC-ROC score of 0.87 indicates that these tests detect genuine drug reactions well. The method effectively reduces the mean absolute error to 0.30. This research highlights how vital it is to apply sophisticated machine learning algorithms to enhance drug predictions, which might impact cancer patients' choices. This strategy helps us comprehend cancer therapy changes by personalizing treatment and improving forecasts. The goal is to improve patient outcomes and advance oncology.