Data_Sheet_1_A Machine Learning Based Dose Prediction of Lutein Supplements for Individuals With Eye Fatigue.XLSX

Purpose: Nutritional intervention was always implemented based on “one-size-fits-all” recommendation instead of personalized strategy. We aimed to develop a machine learning based model to predict the optimal dose of a botanical combination of lutein ester, zeaxanthin, extracts of black currant, chrysanthemum, and goji berry for individuals with eye fatigue.
Methods: 504 features, including demographic, anthropometrics, eye-related indexes, blood biomarkers, and dietary habits, were collected at baseline from 303 subjects in a randomized controlled trial. An aggregated score of visual health (VHS) was developed from total score of eye fatigue symptoms, visuognosis persistence, macular pigment optical density, and Schirmer test to represent an overall eye fatigue level. VHS at 45 days after intervention was predicted by XGBoost algorithm using all features at baseline to show the eye fatigue improvement. Optimal dose of the combination was chosen based on the predicted VHS.
Results: After feature selection and parameter optimization, a model was trained and optimized with a Pearson's correlation coefficient of 0.649, 0.638, and 0.685 in training, test and validation set, respectively. After removing the features collected by invasive blood test and costly optical coherence tomography, the model remained good performance. Among 58 subjects in test and validation sets, 39 should take the highest dose as the optimal option, 17 might take a lower dose, while 2 could not benefit from the combination.
Conclusion: We applied XGBoost algorithm to develop a model which could predict optimized dose of the combination to provide personalized nutrition solution for individuals with eye fatigue.

Purpose：过往营养干预多采用「一刀切」的通用推荐方案，而非个性化策略。本研究旨在构建基于机器学习的模型，为视疲劳人群预测叶黄素酯（lutein ester）、玉米黄质（zeaxanthin）、黑加仑提取物、菊花提取物与枸杞提取物组成的植物复合制剂的最优服用剂量。
Methods：本研究从一项随机对照试验（randomized controlled trial, RCT）的303名受试者基线阶段收集了共504项特征，涵盖人口统计学指标（demographic）、人体测量学指标（anthropometrics）、眼部相关指标、血液生物标志物（blood biomarkers）以及饮食习惯。研究通过视疲劳症状总分、视觉识别持久性、黄斑色素光学密度（macular pigment optical density）与泪液分泌试验（Schirmer test）构建视觉健康综合评分（visual health score, VHS），用以表征受试者整体视疲劳水平。采用XGBoost算法，基于基线阶段的全部特征预测干预后45天的VHS，以反映视疲劳的改善情况；最终基于预测得到的VHS确定该复合制剂的最优服用剂量。
Results：经特征选择与参数优化后，所训练优化的模型在训练集、测试集与验证集上的皮尔逊相关系数（Pearson's correlation coefficient）分别为0.649、0.638与0.685。在移除侵入性血液检测采集的特征以及成本高昂的光学相干断层扫描（optical coherence tomography, OCT）相关特征后，模型仍保持良好性能。在测试集与验证集共58名受试者中，39名最优服用方案为最高剂量，17名可选择较低剂量，剩余2名无法从该复合制剂中获益。
Conclusion：本研究应用XGBoost算法构建模型，可精准预测该复合制剂的最优服用剂量，为视疲劳人群提供个性化营养干预方案。