Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Discover Oncology

DOI: 10.1007/s12672-025-02129-7

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

Institution: Gansu Provincial Maternal and Child Health Hospital

Language: English

Topic: Cutaneous Melanoma Detection and Management

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,690