A multioutput classifier model for breast cancer treatment prediction

Abstract

A growing number of new cases and fatalities occur each year due to breast cancer, making it the most frequent malignancy globally. Utilizing a multioutput classifier technique with algorithms such as CatBoost, XGBoost, NN, and NN Binary, this work presents a new model for predicting breast cancer treatments: surgery, radiotherapy, and chemotherapy. We tackle the pressing need for accurate medical treatments by developing a model to enhance the predicted accuracy of breast cancer treatment outcomes. The model accomplishes impressive results in predicting surgical outcomes; in particular, Neural Networks (NN and NN Binary) perform exceptionally well in terms of recall and precision, reaching 97 % accuracy and 98 % F1-scores. While the model's accuracy is only about 63 % for radiotherapy, it shows a promising recall of up to 84 %. Accuracy and precision in chemotherapy predictions remain stable at 82 %, with AUC-ROC values of up to 89 %, suggesting excellent discrimination ability. By combining multioutput classifiers with sophisticated algorithms, we hope to make treatment prediction models more tailored to individual breast cancer patient profiles, which might usher in a new era of tailored treatment plans and meet the rising demand for precision medicine in cancer care.