Evidence map›Paper›PMID 37333519›Full record

ArticleFrontiers in public health2023

Machine learning to analyze the factors influencing myopia in students of different school periods.

Hao-Jie Tong, Ze-Min Huang, Yu-Lan Li, Yi-Ming Chen, Ben Tian, Ling-Ling Ding, Li-Ling Zhu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 11 citations in OpenAlex.

  1. The application of artificial intelligence in myopia prediction for children and adolescents: a review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Review
  2. Artificial intelligence in pediatric myopia - a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Hao-Jie TongSchool of Public Health, Jiamusi University, Jiamusi, Heilongjiang, China.
Ze-Min HuangSchool of Public Health, Jiamusi University, Jiamusi, Heilongjiang, China.
Yu-Lan LiSchool of Public Health, Jiamusi University, Jiamusi, Heilongjiang, China.
Yi-Ming ChenSchool of Public Health, Jiamusi University, Jiamusi, Heilongjiang, China.
Ben TianSchool of Public Health, Jiamusi University, Jiamusi, Heilongjiang, China.
Ling-Ling DingClinical College of Anhui Medical University, Hefei, Anhui, China.
Li-Ling ZhuSchool of Public Health, Jiamusi University, Jiamusi, Heilongjiang, China.
Jiamusi University · CNAnhui Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: We aim to develop myopia classification models based on machine learning algorithms for each schooling period, and further analyze the similarities and differences in the factors influencing myopia in each school period based on each model. Design: Retrospective cross-sectional study. Participants: We collected visual acuity, behavioral, environmental, and genetic data from 7,472 students in 21 primary and secondary schools (grades 1-12) in Jiamusi, Heilongjiang Province, using visual acuity screening and questionnaires. Methods: Machine learning algorithms were used to construct myopia classification models for students at the whole schooling period, primary school, junior high school, and senior high school period, and to rank the importance of features in each model. Results: The main influencing factors for students differ by school section, The optimal machine learning model for the whole schooling period was Random Forest (AUC = 0.752), with the top three influencing factors being age, myopic grade of the mother, and Whether myopia requires glasses. The optimal model for the primary school period was a Random Forest (AUC = 0.710), with the top three influences being the myopic grade of the mother, age, and extracurricular tutorials weekly. The Junior high school period was an Support Vector Machine (SVM; AUC = 0.672), and the top three influencing factors were gender, extracurricular tutorial subjects weekly, and whether can you do the "three ones" when reading and writing. The senior high school period was an XGboost (AUC = 0.722), and the top three influencing factors were the need for spectacles for myopia, average daily time spent outdoors, and the myopic grade of the mother. Conclusion: Factors such as genetics and eye use behavior all play an essential role in students' myopia, but there are differences between school periods, with those in the lower levels focusing on genetics and those in the higher levels focusing on behavior, but both play an essential role in myopia.

Indexed as

MyopiaCross-Sectional StudiesFemaleHumansMachine LearningRetrospective StudiesSchoolsStudentschildreninfluencing factormachine learningmyopiastudent

Identifiers

PMID37333519
PMCPMC10270291
OpenAlexW4379053900

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.