Aims To use machine learning (ML) to determine the relative contributions of modifiable and non-modifiable clinical, metabolic, genetic, lifestyle and socioeconomic factors on the risk of major eye diseases.
Methods We conducted analyses in a cross-sectional multi-ethnic population-based study (n=10 033 participants) and determined a range of modifiable and non-modifiable risk factors of common eye diseases, including diabetic retinopathy (DR), non-diabetic-related retinopathy (NDR); early and late age-related macular degeneration (AMD); nuclear, cortical and posterior subcapsular (PSC) cataract; and primary open-angle (POAG) and primary angle-closure glaucoma (PACG). Risk factors included individual characteristics, metabolic profiles, genetic background, lifestyle patterns and socioeconomic status (n~100 risk factors). We used gradient boosting machine to estimate the relative influence (RI) of each risk factor.
Results Among the range of risk factors studied, the highest contributions were duration of diabetes for DR (RI=22.1%), and alcohol consumption for NDR (RI=6.4%). For early and late AMD, genetic background (RI~20%) and age (RI~15%) contributed the most. Axial length was the main risk factor of PSC (RI=30.8%). For PACG, socioeconomic factor (mainly educational level) had the highest influence (20%). POAG was the disease with the highest contribution of modifiable risk factors (cumulative RI~35%), followed by PACG (cumulative RI ~30%), retinopathy (cumulative RI between 20% and 30%) and late AMD (cumulative RI ~20%).
Conclusion This study illustrates the utility of ML in identifying factors with the highest contributions. Risk factors possibly amenable to interventions were intraocular pressure (IOP) and Body Mass Index (BMI) for glaucoma, alcohol consumption for NDR and levels of HbA1c for DR.
- Public health
Statistics from Altmetric.com
Contributors Conceived and designed the study: SN, YCT, TYW, CYC. Performed the analysis: SN, LZ, XC, LZ. Interpreted and discussed the results: SN, LZ, XC, LZ, YCT, CV, SM, CS, TYW, CYC. Wrote the first draft of the paper: SN. Critically revised the paper: SN, LZ, XC, LZ, YCT, CV, SM, CS, TYW, CY.
Funding NMRC/CIRG/1488/2018 and NMRC/OFLCG/004a/2018.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement In order to adhere to local IRB guidelines, we regret to inform that we are unable to share data relevant to this study openly. Nevertheless, we respectfully urge interested parties to contact corresponding author for further enquiries if necessary.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.