Predictive Analytics for Chemotherapy Effectiveness Using Machine Learning

Abstract: Chemotherapy is still an important part of treating cancer, but how well it works for each patient depends on things like the type of tumor, their genetics, and their general health. Accurately predicting how chemotherapy will work is necessary to make treatment plans work better, lower side effects, and raise patient mortality rates. This study looks into how machine learning (ML) methods can be used in predictive analytics to figure out how well treatment works. Machine learning models like Support Vector Machines (SVM), Random Forests, and Neural Networks are used to figure out which patients are most likely to respond to or not respond to chemotherapy. They do this by looking at information about each patient's medical history, genomic information, treatment plans, and the characteristics of their tumors. The information used to train and test the model comes from clinical studies and patient records. This makes sure that it has a wide range of cancer types and patient traits. To deal with complicated, high-dimensional data and find key drivers of treatment results, both controlled and untrained learning methods are used together. The models are judged by their performance measures, which include their accuracy, precision, recall, and the area under the receiver operating characteristic curve (AUC). The results show that prediction models based on machine learning can accurately predict how well chemotherapy will work, which lets doctors make personalized treatment plans. The results show that using predictive analytics in clinical decision- making could make cancer procedures more efficient and effective, reducing side effects and improving patient outcomes.