The exponential increase of metabolic syndrome and its association with the risk impact of morbidity and mortality has propitiated the development of tools to diagnose this syndrome early. This work presents a model that is based on prognostic variables to classify Mexicans with metabolic syndrome without blood screening applying machine and deep learning. The data that were used in this study contain health parameters related to anthropometric measurements, dietary information, smoking habit, alcohol consumption, quality of sleep, and physical activity from 2289 participants of the Mexico City Tlalpan 2020 cohort. We use accuracy, balanced accuracy, positive predictive value, and negative predictive value criteria to evaluate the performance and validate different models. The models were separated by gender due to the shared features and different habits. Finally, the highest performance model in women found that the most relevant features were: waist circumference, age, body mass index, waist to height ratio, height, sleepy manner that is associated with snoring, dietary habits related with coffee, cola soda, whole milk, and Oaxaca cheese and diastolic and systolic blood pressure. Men’s features were similar to women’s; the variations were in dietary habits, especially in relation to coffee, cola soda, flavored sweetened water, and corn tortilla consumption. The positive predictive value obtained was 84.7% for women and 92.29% for men. With these models, we offer a tool that supports Mexicans to prevent metabolic syndrome by gender; it also lays the foundation for monitoring the patient and recommending change habits.

中文翻译：

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机器和深度学习可在不进行血液筛查的情况下预测代谢综合征

代谢综合征的指数增加及其与发病率和死亡率的风险影响的关系推动了早期诊断该综合征的工具的发展。这项工作提出了一个基于预后变量的模型，无需进行机器和深度学习的血液筛查即可对患有代谢综合征的墨西哥人进行分类。这项研究中使用的数据包含与来自墨西哥城Tlalpan 2020队列的2289名参与者的人体测量学相关的健康参数，饮食信息，吸烟习惯，饮酒，睡眠质量和体育锻炼。我们使用准确性，平衡准确性，正预测值和负预测值标准来评估性能并验证不同的模型。由于共有的特征和不同的习惯，这些模型按性别分开。最后，在女性中表现最高的模型发现，最相关的特征是：腰围，年龄，体重指数，腰高比，身高，与打s有关的困倦方式，与咖啡，可乐苏打有关的饮食习惯，整体牛奶，瓦哈卡奶酪，舒张压和收缩压。男人的特征与女人的相似。饮食习惯方面的差异很大，尤其是与咖啡，可乐汽水，调味甜水和玉米饼食用有关。女性获得的阳性预测值为84.7％，男性为92.29％。通过这些模型，我们提供了一种工具，可支持墨西哥人预防性别代谢综合征；