, Diana Shubekova, Juhae Kim, Minji Kang , Yoon Jung Park Cataracts are a major cause of visual impairment worldwide, particularly among older adults, with an increasing prevalence due to population aging. Surgery is the primary treatment; however, preventive strategies are crucial for reducing the disease burden. This study aimed to investigate dietary and health-related factors associated with cataract occurrence and develop a predictive model using machine learning. Data were derived from the Korea National Health and Nutrition Examination Survey 2015–2017. The study included 190 women aged 60–79 years: 124 with cataracts and 66 controls. Analyzed variables included sociodemographic, behavioral, chronic disease, and dietary intake factors. After data preprocessing, 4 machine learning algorithms: support vector machine (SVM), random forest (RF), eXtreme gradient boosting, and multilayer perceptron were used. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUROC) and precision-recall curves. Among the tested models, the SVM achieved the best performance under stratified 10-fold cross-validation, with 71% accuracy, 86% precision, 73% recall, 79% F1-score, 65% AUROC, and 81% AUPRC. According to our findings, the odds of having cataracts can be effectively predicted using dietary and health data without relying on specialized ophthalmic equipment. The proposed model demonstrates the potential of machine learning-based tools for early identification and prevention of cataracts. Future studies with larger and more diverse samples, as well as integrating additional data sources such as genomics and lifestyle factors, are warranted to refine predictive accuracy and enhance personalized nutrition-based interventions.
Chronic obstructive pulmonary disease (COPD) is a major respiratory disorder characterized by irreversible airflow limitation. The role of diet in the prevention and management of COPD is receiving increasing attention. This study aimed to examine the association between the composite intake of vegetables, fruits, meat, and fish and pulmonary function as well as COPD prevalence in a representative sample of Korean adults aged ≥ 40 years using data from the 7th Korea National Health and Nutrition Examination Survey. Higher vegetable intake was associated with significantly better pulmonary function parameters, including forced vital capacity (p < 0.001), forced vital capacity percent predicted (p = 0.050), forced expiratory volume (FEV) in 1 second (FEV1; p < 0.001), FEV1 percent predicted (p = 0.038), FEV in 6 seconds (p < 0.001), and peak expiratory flow (p < 0.001). Furthermore, individuals with a high combined intake of vegetables, fruits, meat, and fish demonstrated a 0.261-fold lower COPD prevalence than those without such intake (p = 0.039). The dietary inflammatory index (DII) was significantly lower among participants without COPD than among those with COPD (mean DII = −3.6947, p = 0.002), indicating that a diet rich in anti-inflammatory nutrients can help reduce COPD risk. These findings suggest that vegetable consumption supports improved respiratory function, and a composite dietary pattern incorporating various food groups may help reduce the prevalence of COPD in the adult population.
The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40–69 years from the Korea National Health and Nutrition Examination Survey (2013–2018). We set MetS (3–5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = −2.0545] and saturated fatty acid [β = −2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.
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