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.