Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico.
Instituto de Ciencias Nucleares, UNAM, Mexico City, Mexico.
Nutr Diet. 2019 Feb;76(1):104-109. doi: 10.1111/1747-0080.12495. Epub 2018 Nov 5.
To investigate whether eating patterns of specific food groups can be used to predict and classify Mexican adults who have been diagnosed as having obesity, diabetes or both, when compared to those without a diagnosis. Additionally, we aim to show the benefit of data mining techniques in nutritional studies.
Statistical analysis of self-reported eating patterns based on designated food groups is conducted. Predictive models for health status based on dietary patterns are built using a naïve Bayes classifier.
Clear patterns emerge in the model building where adults are categorised as having obesity, diabetes or both. The model for diabetics showed the greatest degree of predictability, producing sensitivity results 2.4 times higher than the average, using score decile testing. The models for people with obesity and for those with both obesity and diabetes both reported sensitivity doubling the average. Coverage also showed greatest response for the diabetic model, the first decile containing 24% of all diabetics.
Classifier models using dietary habits as inputs succeed in subcategorising Mexican adults based on health status. Diabetics are associated with a very different, and more appropriate dietary pattern (significantly less sugar consumption) for their condition, relative to the non-diagnosed group. Adults with obesity are also associated with a very different, but inappropriate (higher overall consumption), dietary pattern. We hypothesise that obesity, unlike diabetes, is not seen as a sufficiently serious condition, leading to an inadequate response to the diagnosis. Furthermore, data mining techniques can provide new results in nutritional studies.
研究特定食物组的饮食习惯是否可用于预测和分类已被诊断为肥胖、糖尿病或两者兼有（与未被诊断者相比）的墨西哥成年人，并展示数据挖掘技术在营养研究中的优势。
对基于指定食物组的自我报告饮食习惯进行统计分析。使用朴素贝叶斯分类器为基于饮食模式的健康状况构建预测模型。
在模型构建中出现了清晰的模式，将成年人分为肥胖、糖尿病或两者兼有。糖尿病模型的可预测性最强，使用分数十分位数测试，其敏感性结果比平均值高 2.4 倍。肥胖者和肥胖合并糖尿病者的模型的敏感性均为平均值的两倍。覆盖率对糖尿病模型的响应最大，第一个十分位数包含了 24%的糖尿病患者。
使用饮食习惯作为输入的分类器模型成功地根据健康状况对墨西哥成年人进行了细分。与未被诊断组相比，糖尿病患者的饮食模式非常不同，且更适合他们的病情（糖摄入量显著减少）。肥胖成年人也与非常不同但不适当（总摄入量更高）的饮食模式有关。我们假设肥胖不像糖尿病那样被视为严重的疾病，导致对诊断的反应不足。此外，数据挖掘技术可以在营养研究中提供新的结果。
