Comparative analysis of machine learning algorithms for predicting tibial intramedullary nail length from patient characteristics.

ABSTRACT:

Objective

This study aimed to evaluate the performance of five machine learning algorithms in predicting tibial intramedullary nail length using patient demographic data (gender, height, age, and weight), with the goal of developing a clinically relevant and accurate predictive model.

Methods

Retrospective data from 155 patients who underwent tibial intramedullary nailing at the Affiliated Jiangyin Hospital of Nantong University were analyzed. After data cleaning, outlier handling, and gender encoding, the dataset was divided into an 80% training set and 20% testing set. Models were trained and evaluated using root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and correlation analysis. Key variables included height (cm),

SUBMITTER: Hui Y 

PROVIDER: S-EPMC12360990 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature