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Objectives <p>To establish a predictive model for prognosis of cutaneous melanoma using machine learning algorithms in large sample data.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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97 Machine learning algorithms in the prognosis of cutaneous melanoma: a population-based study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月17日

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  • Tongtong Jin,
  • Donggang Yao,
  • Yan Xu,
  • Xiaopeng Zhang,
  • Xu Dong,
  • Haiya Bai

摘要

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.