Food consumption patterns have undergone changes that in recent years have resulted in serious health problems. Studies based on the evaluation of the nutritional status have determined that the adoption of a food pattern-based primarily on a Mediterranean diet (MD) has a preventive role, as well as the ability to mitigate the negative effects of certain pathologies. A group of more than 500 adults aged over 40 years from our cohort in Northwestern Spain was surveyed. Under our experimental design, 10 experiments were run with four different machine-learning algorithms and the predictive factors most relevant to the adherence of a MD were identified. A feature selection approach was explored and under a null hypothesis test, it was concluded that only 16 measures were of relevance, suggesting the strength of this observational study. Our findings indicate that the following factors have the highest predictive value in terms of the degree of adherence to the MD: basal metabolic rate, mini nutritional assessment questionnaire total score, weight, height, bone density, waist-hip ratio, smoking habits, age, EDI-OD, circumference of the arm, activity metabolism, subscapular skinfold, subscapular circumference in cm, circumference of the waist, circumference of the calf and brachial area.

中文翻译：

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通过机器学习技术识别地中海饮食坚持程度的预测因素

近年来，食品消费模式发生了变化，导致了严重的健康问题。基于营养状况评估的研究表明，采用主要以地中海饮食 (MD) 为基础的食物模式具有预防作用，并且能够减轻某些病症的负面影响。我们对西班牙西北部的 500 多名 40 岁以上成年人进行了调查。根据我们的实验设计，使用四种不同的机器学习算法进行了 10 次实验，并确定了与 MD 依从性最相关的预测因素。探索了一种特征选择方法，并在零假设检验下得出结论，只有 16 个度量是相关的，这表明了这项观察性研究的强度。我们的研究结果表明，以下因素对MD的依从程度具有最高的预测价值：基础代谢率、迷你营养评估问卷总分、体重、身高、骨密度、腰臀比、吸烟习惯、年龄、EDI-OD、臂围、活动代谢、肩胛下皮褶、肩胛下周长（厘米）、腰围、小腿周长和肱动脉面积。