Abstract
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.
Highlights
Eye fatigue known as asthenopia, is a common condition in both adults and children, which can be caused by various reasons, especially the intensive use of electronic products e.g., computers, cell phones and iPads [1]
Machine learning algorithms, including random forest (RF), extremely randomized trees (ET), extreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT), usually establish a model from test inputs to make predictions or decisions based on the data [7]
Test and optical coherence tomography (OCT) features were excluded, both were replaced by dietary features (Figure 4B)
Summary
Eye fatigue known as asthenopia, is a common condition in both adults and children, which can be caused by various reasons, especially the intensive use of electronic products e.g., computers, cell phones and iPads [1]. Dose Prediction of Lutein Supplements rich anthocyanin, were reported to relieve eye fatigue [4]. Most strategies for preventing or reducing the incidence of the symptoms are based on “one size fits all” public health recommendations to the whole population. Machine learning as a field of computer science adopts computer algorithms to identify patterns in large datasets with numerous variables, which can be used to predict data-based outcomes [6]. Machine learning algorithms, including random forest (RF), extremely randomized trees (ET), extreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT), usually establish a model from test inputs to make predictions or decisions based on the data [7]. Machine learning techniques have proven to be highly effective for prediction of response to methotrexate and antidepressant medication, and diagnoses of pediatric diseases and upper gastrointestinal cancer [7,8,9,10]
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