The machine learning components revealed the effects of the clinical characteristics of patients with breast cancer on second primary malignancies.

<p>The feature importance and SHAP values of the clinical characteristics of second primary malignancies based on the results of XGBoost (A), k-nearest neighbor (B), light gradient boosting machine (C), logistic regression (D), support vector machine (E), neural network (F), and random forest...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Yulin Lai (12031541) (author)
مؤلفون آخرون:	Peiyuan Huang (2618038) (author)
منشور في:	2025
الموضوعات:	Medicine Cell Biology Biotechnology Developmental Biology Marine Biology Cancer Science Policy Plant Biology Biological Sciences not elsewhere classified support vector machine second primary malignancy second primary malignancies machine learning identifies 94 &# 8211 significantly greater risks including ridge regression important factors affecting standard adjuvant port xlink "> among 3017 developed spms breast cancer patients xlink "> breast cancer logistic regression risk factors greater risk ywbc individuals ywbc ). term survival spm associated random forest predictive model prediction nomogram postoperative radiotherapy neural network nearest neighbor may increase main treatments favorable histology end results cumulative incidence clinical characteristics 88 ). 48 ),
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The machine learning components revealed the effects of the clinical characteristics of patients with breast cancer on second primary malignancies.

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