January 16, 2022Open Access

Development and Validation of an Insulin Resistance Predicting Model Using a Machine-Learning Approach in a Population-Based Cohort in Korea

SPSunmin ParkHallym University CKChaeyeon KimPohang University of Science and Technology XWXuangao WuHoseo University

Structured PICO

Does a machine-learning model accurately predict insulin resistance in Koreans aged over 40 years?

Population

Koreans aged over 40 years in a population-based cohort

Intervention

Insulin resistance predicting model using a machine-learning approach

Outcome

Prediction of insulin resistance

A machine-learning model incorporating liver function, pulse, seasonal variation, and metabolic syndrome components can predict insulin resistance in Koreans over 40.

Abstract

The liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years.

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