PURPOSE: To compare the accuracy of classifying prediabetes, include type 2 diabetes, predicted by hand-grip strength employing machine learning (ML) techniques in Korean obese adults. METHODS: The data of 1230 Korean obese adults (51.5% males, 19-65yrs) was retrieved from the Korean National Health and Nutrition Examination Survey (KNHANES) 2014-2015. Obesity was identified by the Korean standard BMI (BMI ≥ 25kg/m2) and waist circumference (WC; Male: WC ≥ 90cm, Female: WC ≥ 85cm). A total of 591 individuals with prediabetes and type 2 diabetes was diagnosed by the criteria 1) diagnosed as type 2 diabetes, 2) using diabetes medication, 3) abnormal fasting glucose level (fasting glucose ≥ 100mg/dL). Three grip strength models were employed, which were 1) normal grip strength (GS), 2) divided by weight (GSW), and 3) divided by BMI (GSB), respectively, to examine the effect of different relative grip strengths on prediabetes. Multilayer perceptron (MLP) and traditional logistic regression (LR) algorithms employing RSNNS package in R, were applied to classify the prediabetes predicted by hand-grip adjusted by age, income, education, occupation, marital status, binge drinking, smoking, daily calories intake, sedentary time, strengthening exercise adherence, aerobic exercise adherence variables, separated by gender. To evaluate the accuracy (ACC), sensitivity (SEN), and specificity (SPE) of the confusion matrix of ML, the participants were separated into a train for deriving equations and a test group for holdout cross-validation. RESULTS: To classify prediabetes in obese males, GSB adjusted by co-variates was revealed the highest ACC for both ML classifiers (train group: MLP=71.5%, LR=67.6%; test group: MLP=63.9%, LR=66.1%), and GSW showed the lowest ACC (MLP: train=67.6%, test=61.6%). Moreover, GSW and GSB of MLP showed higher SEN than SPE in test group (SEN, SPE: GSW=78.6%, 44.1%; GSB=74.4%, 52.5%, respectively). In obese females, however, both ML classifiers did not show any consistent accuracy levels. CONCLUSIONS: It was revealed that GSB showed a strong relationship with prediabetes in obese male adults in Korea. Moreover, MLP and LR classifiers present fair accuracy in the cross-validation to classify prediabetes predicted by GSB.