Tegaw Eyachew Misganew, Asfaw Betelhem Bizuneh
Department of Physics, College of Natural and Computational Sciences, Debre Tabor University, 272, Debre Tabor, Ethiopia.
Department of Health System Management and Health Economics, School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia.
Sci Rep. 2025 Jul 10;15(1):24981. doi: 10.1038/s41598-025-97763-0.
Breast cancer continues to be a leading cause of death among women in the world. The prediction of survival outcomes based on treatment modalities, i.e., chemotherapy, hormone therapy, surgery, and radiation therapy is an essential step towards personalization in treatment planning. However, Machine Learning (ML) models may improve these predictions by investigating intricate relationships between clinical variables and survival. This study investigates the performance of several ML models to predict survival rate in patients undergoing diverse breast cancer treatments i.e., chemotherapy, hormone therapy, surgery and radiation using multiple clinical parameters. The dataset consisted of 5000 samples and turned into downloaded from Kaggle. The models assessed blanketed Support Vector Machines (SVM), K-Nearest Neighbor (KNN), AdaBoost, Gradient Boosting, Random Forest, Gaussian Naive Bayes, Logistic Regression, Extreme Gradient Boosting (XG boost), and Decision tree. Performance of the models was assessed using parameters such as Accuracy, Precision, Recall, F1-Score and Area under the Receiver Operating Characteristic Curve (AUC-ROC). SHAP (SHapley Additive exPlanations) values analysis was done to provide an explanation for the impact of a feature on model predictions using Waterfall and Beeswarm plots. Anticipated baseline (E(f(x))) were in comparison to the predictions (f(x)) for each therapy group. Performance of Gradient Boosting was shown to be the best with an Accuracy: 0.972, Precision: 0.973, Recall: 0.972, F1-Score: 0.973, and AUC-ROC Score: 0.997. Chemotherapy had a notably bad impact on survival, with an f(x) of -0.274 and an E(f(x)) of -0.025. Hormone therapy showed the maximum detrimental effect on survival, with an f(x) of -0.408. Surgical operation had an extraordinarily impartial impact (f(x) = -0.041), even as radiation therapy positively impacted survival results with an f(x) of 0.22. Gradient Boosting was the most predictive algorithm for breast cancer survival outcomes. This SHAP-primarily based analysis provides a complete knowledge of ways one-of-a-kind treatments have an effect on survival predictions in breast cancer patients. Radiation therapy indicates the most tremendous effect on survival, whilst hormone therapy reveals the maximum poor effect. Future studies need to explore personalized treatment strategies that comprise these insights to enhance patient effects.
乳腺癌仍然是全球女性死亡的主要原因之一。基于治疗方式（即化疗、激素治疗、手术和放射治疗）预测生存结果是治疗计划个性化的关键一步。然而，机器学习（ML）模型可以通过研究临床变量与生存之间的复杂关系来改进这些预测。本研究使用多个临床参数，调查了几种ML模型对接受不同乳腺癌治疗（即化疗、激素治疗、手术和放射治疗）患者的生存率进行预测的性能。数据集包含5000个样本，从Kaggle下载。评估的模型包括支持向量机（SVM）、K近邻（KNN）、AdaBoost、梯度提升、随机森林、高斯朴素贝叶斯、逻辑回归、极端梯度提升（XG boost）和决策树。使用准确率、精确率、召回率、F1分数和受试者工作特征曲线下面积（AUC-ROC）等参数评估模型性能。进行SHAP（Shapley加性解释）值分析，以使用瀑布图和蜂群图解释特征对模型预测的影响。将预期基线（E(f(x))）与每个治疗组的预测（f(x)）进行比较。结果显示梯度提升的性能最佳，准确率为0.972，精确率为0.973，召回率为0.972，F1分数为0.973，AUC-ROC分数为0.997。化疗对生存率有显著负面影响，f(x)为-0.274，E(f(x))为-0.025。激素治疗对生存率的负面影响最大，f(x)为-0.408。手术的影响非常中性（f(x)=-0.041），而放射治疗对生存结果有积极影响，f(x)为0.22。梯度提升是预测乳腺癌生存结果最有效的算法。这种基于SHAP的分析全面了解了不同治疗方法对乳腺癌患者生存预测的影响。放射治疗对生存的影响最大，而激素治疗的负面影响最大。未来的研究需要探索包含这些见解的个性化治疗策略，以改善患者预后。
