ArticleComputational and mathematical methods in medicine2021
Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Early Detection of Keratoconus: Diagnostic Advances and Their Impact on Visual Outcomes: A Systematic Review.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Artificial intelligence for detecting keratoconus.The Cochrane database of systematic reviews · 2023Pooled it
- Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Machine learning-assisted early detection of keratoconus: a comparative analysis of corneal topography and biomechanical data.Scientific reports · 2025Article
- Highly efficient stacking ensemble learning model for automated keratoconus screening.Eye and vision (London, England) · 2025Article
- Advances in machine learning for keratoconus diagnosis.International ophthalmology · 2025Review
- Smart decision support system for keratoconus severity staging using corneal curvature and thinnest pachymetry indices.Eye and vision (London, England) · 2024Article
- Artificial intelligence as diagnostic modality for keratoconus: A systematic review and meta-analysis.Journal of Taibah University Medical Sciences · 2024Review
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
- Keratoconus: imaging modalities and management.Medical hypothesis, discovery & innovation ophthalmology journal · 2024Review
- Development and validation to predict visual acuity and keratometry two years after corneal crosslinking with progressive keratoconus by machine learning.Frontiers in medicine · 2023Article
- Advances in artificial intelligence models and algorithms in the field of optometry.Frontiers in cell and developmental biology · 2023Review
- Artificial intelligence-assisted diagnosis of ocular surface diseases.Frontiers in cell and developmental biology · 2023Review
- Artificial intelligence and corneal diseases.Current opinion in ophthalmology · 2022Review
- Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients.Frontiers in endocrinology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does not feel any pain. Therefore, the development of a method for detecting this disease based on machine and deep learning methods is necessary for early detection in order to provide the appropriate treatment as early as possible to patients. Thus, the objective of this work is to determine the most relevant parameters with respect to the different classifiers used for keratoconus classification based on the keratoconus dataset of Harvard Dataverse. A total of 446 parameters are analyzed out of 3162 observations by 11 different feature selection algorithms. Obtained results showed that sequential forward selection (SFS) method provided a subset of 10 most relevant variables, thus, generating the highest classification performance by the application of random forest (RF) classifier, with an accuracy of 98% and 95% considering 2 and 4 keratoconus classes, respectively. Found classification accuracy applying RF classifier on the selected variables using SFS method achieves the accuracy obtained using all features of the original dataset.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.