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Supervised Machine Learning Predictive Analytics For Triple
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OBJECTIVE
To use machine learning algorithms to predict the death outcomes of patients with triple-negative breast cancer, 5 years after discharge.
METHODS
1570 stage I-III breast cancer patients receiving treatment from Sun Yat-sen Memorial Hospital were analyzed. Machine learning was used to predict the death outcomes of patients with triple-negative breast cancer, 5 years after discharge.
RESULTS
The results showed that platelets, LMR (lymphocyte-to-monocyte ratio), age, PLR (the platelet-to-lymphocyte ratio) and white blood cell counts accounted for a significant weight in the 5-year prognosis of triple-negative breast cancer patients. The results of model prediction indicated that rankings for accuracy among the training group (from high to low) were forest, gbm, and DecisionTree (0.770335, 0.760766, 0.751994, 0.737640 and 0.734450, respectively). For AUC value (high to low), they were forest, Logistic and DecisionTree (0.896673, 0.895408, 0.776836, 0.722799 and 0.702804, respectively). The highest MSE value for DecisionTree was 0.2656, and the lowest MSE value for forest was 0.2297. In the test group, accuracy rankings (from high to low) were DecisionTree, and GradientBoosting (0.748408, 0.738854, 0.738854, 0.732484 and gbm, respectively). For AUC value (high to low), the rankings were GradientBoosting, gbm, and DecisionTree (0.731595, 0.715438, 0.712767, 0.708348 and 0.691960, respectively). The maximum MSE value for gbm was 0.2707, and the minimum MSE value for DecisionTree was 0.2516.
CONCLUSION
The machine learning algorithm can predict the death outcomes of patients with triple-negative breast cancer 5 years after discharge. This can be used to estimate individual outcomes for patients with triple-negative breast cancer.
中文翻译：
目的 使用机器学习算法预测三阴性乳腺癌患者出院后 5 年的死亡结局。方法对1570例在中山纪念医院接受治疗的I-III期乳腺癌患者进行分析。机器学习用于预测三阴性乳腺癌患者出院后 5 年的死亡结果。结果结果显示，血小板、LMR（淋巴细胞与单核细胞的比率）、年龄、PLR（血小板与淋巴细胞的比率）和白细胞计数在三阴性乳腺癌的5年预后中占显着权重。癌症患者。模型预测结果表明，训练组的准确率排名（从高到低）分别为 Forest、gbm 和 DecisionTree（0.770335、0.760766、0.751994、0.737640 和 0.734450，分别）。对于 AUC 值（从高到低），它们是森林、Logistic 和决策树（分别为 0.896673、0.895408、0.776836、0.722799 和 0.702804）。决策树的最高 MSE 值为 0.2656，森林的最低 MSE 值为 0.2297。在测试组中，准确率排名（从高到低）分别为 DecisionTree 和 GradientBoosting（分别为 0.748408、0.738854、0.738854、0.732484 和 gbm）。对于 AUC 值（从高到低），排名分别为 GradientBoosting、gbm 和 DecisionTree（分别为 0.731595、0.715438、0.712767、0.708348 和 0.691960）。gbm 的最大 MSE 值为 0.2707，DecisionTree 的最小 MSE 值为 0.2516。结论机器学习算法可以预测三阴性乳腺癌患者出院5年后的死亡结局。 |
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