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http://dx.doi.org/10.13048/jkm.20015

Application of Machine Learning to Predict Weight Loss in Overweight, and Obese Patients on Korean Medicine Weight Management Program  

Kim, Eunjoo (Nubebe Mibyeong Research Institute)
Park, Young-Bae (Nubebe Mibyeong Research Institute)
Choi, Kahye (Nubebe Mibyeong Research Institute)
Lim, Young-Woo (Nubebe Mibyeong Research Institute)
Ok, Ji-Myung (Nubebe Mibyeong Research Institute)
Noh, Eun-Young (Department of Medical Science of Meridian, College of Korean Medicine, Graduate School, Kyung Hee University)
Song, Tae Min (Department of Health Management, Sahmyook University)
Kang, Jihoon (Department of Business Administration, Korea Polytechnic University)
Lee, Hyangsook (Department of Anatomy, College of Korean Medicine, Kyung Hee University)
Kim, Seo-Young (Nubebe Mibyeong Research Institute)
Publication Information
The Journal of Korean Medicine / v.41, no.2, 2020 , pp. 58-79 More about this Journal
Abstract
Objectives: The purpose of this study is to predict the weight loss by applying machine learning using real-world clinical data from overweight and obese adults on weight loss program in 4 Korean Medicine obesity clinics. Methods: From January, 2017 to May, 2019, we collected data from overweight and obese adults (BMI≥23 kg/m2) who registered for a 3-month Gamitaeeumjowi-tang prescription program. Predictive analysis was conducted at the time of three prescriptions, and the expected reduced rate and reduced weight at the next order of prescription were predicted as binary classification (classification benchmark: highest quartile, median, lowest quartile). For the median, further analysis was conducted after using the variable selection method. The data set for each analysis was 25,988 in the first, 6,304 in the second, and 833 in the third. 5-fold cross validation was used to prevent overfitting. Results: Prediction accuracy was increased from 1st to 2nd and 3rd analysis. After selecting the variables based on the median, artificial neural network showed the highest accuracy in 1st (54.69%), 2nd (73.52%), and 3rd (81.88%) prediction analysis based on reduced rate. The prediction performance was additionally confirmed through AUC, Random Forest showed the highest in 1st (0.640), 2nd (0.816), and 3rd (0.939) prediction analysis based on reduced weight. Conclusions: The prediction of weight loss by applying machine learning showed that the accuracy was improved by using the initial weight loss information. There is a possibility that it can be used to screen patients who need intensive intervention when expected weight loss is low.
Keywords
Machine learning; Obesity; Weight loss; Artificial intelligence;
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Times Cited By KSCI : 9  (Citation Analysis)
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