A machine learning-based approach to predicting the malignant and metastasis of thyroid cancer.

ABSTRACT:

Background

Thyroid Cancer (TC) is the most common malignant disease of endocrine system, and its incidence rate is increasing year by year. Early diagnosis, management of malignant nodules and scientific treatment are crucial for TC prognosis. The first aim is the construction of a classification model for TC based on risk factors. The second aim is the construction of a prediction model for metastasis based on risk factors.

Methods

We retrospectively collected approximately 70 preoperative demographic and laboratory test indices from 1735 TC patients. Machine learning pipelines including linear regression model ridge, Logistic Regression (LR) and eXtreme Gradient Boosting (XGBoost) were used to select the best model for predicting deterioration and metastasis of TC. A compr

SUBMITTER: Gu J 

PROVIDER: S-EPMC9806162 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature