Construction and validation of machine learning models for predicting lymph node metastasis in cutaneous malignant melanoma: a large population-based study.

ABSTRACT:

Background

Lymph node status is essential for determining the prognosis of cutaneous malignant melanoma (CMM). This study aimed to develop a machine learning (ML) model for predicting lymph node metastases (LNM) in CMM.

Methods

We gathered data on 6,196 patients from the Surveillance, Epidemiology, and End Results (SEER) database, including known clinicopathologic variables, using six ML algorithms, including logistic regression (LR), support vector machine (SVM), Complement Naive Bayes (CNB), Extreme Gradient Boosting (XGBoost), RandomForest (RF), and k-nearest neighbor algorithm (kNN), to predict the presence of LNM in CMM. Subsequently, we established prediction models. The utilization of the adaptive synthetic (ADASYN) method served to address the challenge posed by imba

SUBMITTER: Lan LF 

PROVIDER: S-EPMC11912072 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature