摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据中国人民共和国合肥的新闻报道，NewsRx编辑的研究表明：“这项研究旨在利用容易收集的个人信息，开发一种预测14-17岁ADO患者超重发病的机器学习模型。这项研究是一项为期一年的前瞻性队列研究。”我们的新闻记者引用了中国科学院的研究，“基线数据是通过人体测量和问卷调查收集的，”在1241名青少年中,采用SHapley相加解释法(SHAP)对模型进行全局和局部解释,通过单因素分析筛选预测因素,建立了6个预测超重的机器学习模型,并对模型进行了全局和局部解释。204例(16.4%)在一年后被确定为超重,19个特征与超重发生率相关,按7:3的比例随机分为训练组和测试组,Light Gradient Boosting Machine(LGBM)算法在准确率(0.956)、回忆率(0.812)、特异性(0.983)、F1-评分(0.855)、AUC(0.961).重要性排序表明,前11个最小特征集可以保持模型性能的稳定性.利用易于收集的个人信息可以准确预测青少年超重的发生.基于lgbm的模型表现出更好的性能.过度采样技术显著提高了模型性能.
Abstract
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."
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