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Genetic Prediction of Drug Toxicity in Cervical Cancer Using Machine Learning

Lu Wang, Wei Guo, Xiaodong Lee

Cervical cancer is a significant global health concern, necessitating the development of effective therapeutic strategies. However, the success of these strategies is often hindered by drug toxicity, which can lead to adverse effects and treatment discontinuation. Genetic variations among patients play a crucial role in their susceptibility to drug toxicity. In recent years, machine learning techniques have demonstrated remarkable potential in predicting drug responses based on genetic information. In this study, we present a novel approach to predict drug toxicity in cervical cancer patients using machine learning algorithms and genetic data. By leveraging comprehensive genetic profiles and drug toxicity information, we aim to enhance personalized treatment strategies and mitigate the occurrence of adverse drug reactions. This research holds promise in improving the safety and efficacy of cervical cancer treatments, ultimately contributing to better patient outcomes and quality of life.