Sepidan Bagherololoom Health Higher Education College, Shiraz University of Medical Sciences, Shiraz, Iran; Metabolic and Cardiovascular Diseases Laboratory, Division of Human Nutrition, University of Alberta, Edmonton, AB T6G2P5, Canada.
Department of Computer Science, St. Francis Xavier University, Nova Scotia, Canada.
Clin Nutr ESPEN. 2023 Jun;55:136-143. doi: 10.1016/j.clnesp.2023.02.011. Epub 2023 Feb 22.
BACKGROUND & AIMS: Premenstrual syndrome (PMS) is a common disorder affecting 30-40% of women of reproductive age. Many modifiable risk factors associated with PMS involve nutrition and poor eating habits. This study aims to explore the correlation between micronutrients and PMS in a group of Iranian women and to build a predictor model showing the PMS using nutritional and anthropometric variables.
This is a cross-sectional study which was conducted on 223 females in Iran. Anthropometric indices were measured, including Body Mass Index (BMI) and skinfold thickness. Machine learning methods were used to assess participants' dietary intakes, Food Frequency Questionnaire (FFQ) and analyze the data.
After applying different variable selection techniques, we have created machine learning models such as KNN. KNN achieved 80.3% accuracy rate and 76.3% F1 score indicating that our model is a curate and valid proof to show a strong relationship between input variables (sodium intake, Skin fold thickness of suprailiac, irregular menstruation, total calorie intake, total fiber intake, trans fatty acids, painful menstruation (dysmenorrhea), total sugar intake, total fat intake, and biotin) and the output variable (PMS). We sorted these effective variables based on their 'Shapley values' and figured out that Na intake, suprailiac skinfold thickness, biotin intake, total fat intake and total sugar intake have a major impact on having PMS.
Dietary intake and anthropometric measurements are highly associated with the occurrence of PMS, and in our model, these variables can predict PMS in women with a high accuracy rate.
经前期综合征（PMS）是一种常见疾病，影响 30-40%的育龄期妇女。许多与 PMS 相关的可改变风险因素涉及营养和不良饮食习惯。本研究旨在探讨一组伊朗女性中微量营养素与 PMS 之间的相关性，并建立一个使用营养和人体测量学变量预测 PMS 的模型。
这是一项在伊朗进行的横断面研究，共纳入 223 名女性。测量了人体测量学指标，包括体重指数（BMI）和皮褶厚度。使用机器学习方法评估参与者的膳食摄入量，使用食物频率问卷（FFQ）进行数据分析。
在应用不同的变量选择技术后，我们创建了机器学习模型，如 KNN。KNN 达到了 80.3%的准确率和 76.3%的 F1 分数，表明我们的模型是一个经过精心制作和有效的证明，表明输入变量（钠摄入量、髂上皮褶厚度、月经不规律、总热量摄入、总纤维摄入量、反式脂肪酸、痛经（痛经）、总糖摄入量、总脂肪摄入量和生物素）与输出变量（PMS）之间存在很强的关系。我们根据这些有效变量的“Shapley 值”对它们进行排序，并发现钠摄入量、髂上皮褶厚度、生物素摄入量、总脂肪摄入量和总糖摄入量对 PMS 的发生有重大影响。
饮食摄入和人体测量学测量与 PMS 的发生高度相关，在我们的模型中，这些变量可以以高准确率预测女性的 PMS。
