A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional Study
Abstract BackgroundAnemia is a global public health issue causing symptoms such as fatigue, weakness, and cognitive decline. Furthermore, anemia is associated with various diseases and increases the risk of postoperative complications and mortality. Frequent invasive blood tes...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-02-01
|
| Series: | JMIR Medical Informatics |
| Online Access: | https://medinform.jmir.org/2025/1/e64204 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850191626337517568 |
|---|---|
| author | Yahan Zhang Yi Chun Hongyuan Fu Wen Jiao Jizhang Bao Tao Jiang Longtao Cui Xiaojuan Hu Ji Cui Xipeng Qiu Liping Tu Jiatuo Xu |
| author_facet | Yahan Zhang Yi Chun Hongyuan Fu Wen Jiao Jizhang Bao Tao Jiang Longtao Cui Xiaojuan Hu Ji Cui Xipeng Qiu Liping Tu Jiatuo Xu |
| author_sort | Yahan Zhang |
| collection | DOAJ |
| description |
Abstract
BackgroundAnemia is a global public health issue causing symptoms such as fatigue, weakness, and cognitive decline. Furthermore, anemia is associated with various diseases and increases the risk of postoperative complications and mortality. Frequent invasive blood tests for diagnosis also pose additional discomfort and risks to patients.
ObjectiveThis study aims to assess the facial spectral characteristics of patients with anemia and to develop a predictive model for anemia risk using machine learning approaches.
MethodsBetween August 2022 and September 2023, we collected facial image data from 78 anemic patients who met the inclusion criteria from the Hematology Department of Shanghai Hospital of Traditional Chinese Medicine. Between March 2023 and September 2023, we collected data from 78 healthy adult participants from Shanghai Jiading Community Health Center and Shanghai Gaohang Community Health Center. A comprehensive statistical analysis was performed to evaluate differences in spectral characteristics between the anemic patients and healthy controls. Then, we used 10 different machine learning algorithms to create a predictive model for anemia. The least absolute shrinkage and selection operator was used to analyze the predictors. We integrated multiple machine learning classification models to identify the optimal model and developed Shapley additive explanations (SHAP) for personalized risk assessment.
ResultsThe study identified significant differences in facial spectral features between anemic patients and healthy controls. The support vector machine classifier outperformed other classification models, achieving an accuracy of 0.875 (95% CI 0.825-0.925) for distinguishing between the anemia and healthy control groups. In the SHAP interpretation of the model, forehead-570 nm, right cheek-520 nm, right zygomatic-570 nm, jaw-570 nm, and left cheek-610 nm were the features with the highest contributions.
ConclusionsFacial spectral data demonstrated clinical significance in anemia diagnosis, and the early warning model for anemia risk constructed based on spectral information demonstrated a high accuracy rate. |
| format | Article |
| id | doaj-art-c8e5797d28104e878cf3b7dd1804772e |
| institution | OA Journals |
| issn | 2291-9694 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Medical Informatics |
| spelling | doaj-art-c8e5797d28104e878cf3b7dd1804772e2025-08-20T02:14:51ZengJMIR PublicationsJMIR Medical Informatics2291-96942025-02-0113e64204e6420410.2196/64204A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional StudyYahan Zhanghttp://orcid.org/0009-0005-0985-8040Yi Chunhttp://orcid.org/0000-0002-5106-7489Hongyuan Fuhttp://orcid.org/0000-0003-4193-5681Wen Jiaohttp://orcid.org/0000-0002-6957-9909Jizhang Baohttp://orcid.org/0000-0002-1154-1000Tao Jianghttp://orcid.org/0000-0002-4438-5075Longtao Cuihttp://orcid.org/0000-0002-8289-7610Xiaojuan Huhttp://orcid.org/0000-0001-5581-0797Ji Cuihttp://orcid.org/0000-0002-7111-7201Xipeng Qiuhttp://orcid.org/0000-0001-7163-5247Liping Tuhttp://orcid.org/0000-0002-6029-9775Jiatuo Xuhttp://orcid.org/0000-0002-3498-2132 Abstract BackgroundAnemia is a global public health issue causing symptoms such as fatigue, weakness, and cognitive decline. Furthermore, anemia is associated with various diseases and increases the risk of postoperative complications and mortality. Frequent invasive blood tests for diagnosis also pose additional discomfort and risks to patients. ObjectiveThis study aims to assess the facial spectral characteristics of patients with anemia and to develop a predictive model for anemia risk using machine learning approaches. MethodsBetween August 2022 and September 2023, we collected facial image data from 78 anemic patients who met the inclusion criteria from the Hematology Department of Shanghai Hospital of Traditional Chinese Medicine. Between March 2023 and September 2023, we collected data from 78 healthy adult participants from Shanghai Jiading Community Health Center and Shanghai Gaohang Community Health Center. A comprehensive statistical analysis was performed to evaluate differences in spectral characteristics between the anemic patients and healthy controls. Then, we used 10 different machine learning algorithms to create a predictive model for anemia. The least absolute shrinkage and selection operator was used to analyze the predictors. We integrated multiple machine learning classification models to identify the optimal model and developed Shapley additive explanations (SHAP) for personalized risk assessment. ResultsThe study identified significant differences in facial spectral features between anemic patients and healthy controls. The support vector machine classifier outperformed other classification models, achieving an accuracy of 0.875 (95% CI 0.825-0.925) for distinguishing between the anemia and healthy control groups. In the SHAP interpretation of the model, forehead-570 nm, right cheek-520 nm, right zygomatic-570 nm, jaw-570 nm, and left cheek-610 nm were the features with the highest contributions. ConclusionsFacial spectral data demonstrated clinical significance in anemia diagnosis, and the early warning model for anemia risk constructed based on spectral information demonstrated a high accuracy rate.https://medinform.jmir.org/2025/1/e64204 |
| spellingShingle | Yahan Zhang Yi Chun Hongyuan Fu Wen Jiao Jizhang Bao Tao Jiang Longtao Cui Xiaojuan Hu Ji Cui Xipeng Qiu Liping Tu Jiatuo Xu A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional Study JMIR Medical Informatics |
| title | A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional Study |
| title_full | A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional Study |
| title_fullStr | A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional Study |
| title_full_unstemmed | A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional Study |
| title_short | A Risk Warning Model for Anemia Based on Facial Visible Light Reflectance Spectroscopy: Cross-Sectional Study |
| title_sort | risk warning model for anemia based on facial visible light reflectance spectroscopy cross sectional study |
| url | https://medinform.jmir.org/2025/1/e64204 |
| work_keys_str_mv | AT yahanzhang ariskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT yichun ariskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT hongyuanfu ariskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT wenjiao ariskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT jizhangbao ariskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT taojiang ariskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT longtaocui ariskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT xiaojuanhu ariskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT jicui ariskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT xipengqiu ariskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT lipingtu ariskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT jiatuoxu ariskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT yahanzhang riskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT yichun riskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT hongyuanfu riskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT wenjiao riskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT jizhangbao riskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT taojiang riskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT longtaocui riskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT xiaojuanhu riskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT jicui riskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT xipengqiu riskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT lipingtu riskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy AT jiatuoxu riskwarningmodelforanemiabasedonfacialvisiblelightreflectancespectroscopycrosssectionalstudy |