首页|Chinese Academy of Sciences Reports Findings in Machine Learning (Predicting the onset of overweight in Chinese high school students: a machine-learning approac h in a one-year prospective cohort study)
Chinese Academy of Sciences Reports Findings in Machine Learning (Predicting the onset of overweight in Chinese high school students: a machine-learning approac h in a one-year prospective cohort study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Hefei, People's Republ ic of China, by NewsRx editors, research stated, "This study aimed to develop an d evaluate machine-learning models for predicting the onset of overweight in ado lescents aged 14-17, utilizing easily collectible personal information. This stu dy was a one-year prospective cohort study." Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, "Baseline data were collected through anthropometric measurements and questionnaires, and the incidence of overweight was calculated one year late r via anthropometric measurements. Predictive factors were selected through univ ariate analysis. Six machine-learning models were developed for predicting the o nset of overweight. The SHapley Additive exPlanations (SHAP) was used for global and local interpretation of the models. Out of 1,241 adolescents, 204 (16.4% ) were identified as overweight after one year. Nineteen features were associate d with the overweight incidence in univariable analysis. Participants were rando mly divided into a training group and a testing group in a 7:3 ratio. The Light Gradient Boosting Machine (LGBM) algorithm achieved outperformed other models, a chieving the following metrics: Accuracy (0.956), Recall (0.812), Specificity (0 .983), F1-score (0.855), AUC (0.961). Importance ranking revealed that the top 1 1 minimal feature set can maintain the stability of model performance. The onset of overweight in adolescents was accurately predicted using easily collectible personal information. The LGBM-based model exhibited superior performance. Overs ampling technique notably improved model performance."
HefeiPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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