97 种机器学习算法在皮肤黑色素瘤预后中的应用：一项基于人群的研究。
97 Machine learning algorithms in the prognosis of cutaneous melanoma: a population-based study.
作者信息
Jin Tongtong, Yao Donggang, Xu Yan, Zhang Xiaopeng, Dong Xu, Bai Haiya
机构信息
Department of Burns and Plastic Surgery, Gansu Provincial Maternity and Child-care Hospital (Gansu Provincial Central Hospital), Lanzhou, 730050, China.
出版信息
Discov Oncol. 2025 Mar 17;16(1):342. doi: 10.1007/s12672-025-02129-7.
OBJECTIVES
To establish a predictive model for prognosis of cutaneous melanoma using machine learning algorithms in large sample data.
METHODS
A retrospective analysis of patients diagnosed with cutaneous melanoma in the SEER database from 2010 to 2015 was performed using 12 different machine learning algorithms, for a total of 97 algorithm combinations, to screen for variables associated with cutaneous melanoma prognosis and to build predictive models.
RESULTS
A total of 24,457 cases were collected in this study, and 8,441 cases were finally included. Among them, 5908 cases in the training set and 2533 cases in the test set. The results of the study show that StepCox[both] + RSF is the best model. The variable features screened by the best model were Sex, Age, Marital, T stage, N stage, Ulcer, Site, Histologic, Surgery, Chemotherapy, Bone metastasis, Liver metastasis and Lung metastasis.
CONCLUSION
We have developed a predictive model with good accuracy for cutaneous melanoma prognosis using a combination of 97 machine learning algorithms in a large sample database.
目的
利用机器学习算法在大样本数据中建立皮肤黑色素瘤预后的预测模型。
方法
对2010年至2015年SEER数据库中诊断为皮肤黑色素瘤的患者进行回顾性分析，使用12种不同的机器学习算法，共97种算法组合，筛选与皮肤黑色素瘤预后相关的变量并建立预测模型。
结果
本研究共收集24457例病例，最终纳入8441例。其中，训练集5908例，测试集2533例。研究结果表明，StepCox[两者] + RSF是最佳模型。最佳模型筛选出的变量特征为性别、年龄、婚姻状况、T分期、N分期、溃疡、部位、组织学类型、手术、化疗、骨转移、肝转移和肺转移。
结论
我们在大样本数据库中使用97种机器学习算法的组合开发了一种对皮肤黑色素瘤预后具有良好准确性的预测模型。
Zotero 插件