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Advances in Corneal Diagnostics Using Machine Learning.

Noor T Al-Sharify1,2, Salman Yussof3, Nebras H Ghaeb4

  • 1Department of Electrical & Electronic Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Malaysia.

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Summary
This summary is machine-generated.

Machine learning models, Decision Tree and Nearest Neighbor Analysis, improve keratoconus diagnosis using corneal topography data. These tools aid in understanding disease progression and clinical decision-making for better patient outcomes.

Keywords:
corneal asphericitycorneal topographydecision tree and nearest neighbordiagnostic modelsophthalmologyvision health

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Area of Science:

  • Ophthalmology
  • Medical Diagnostics
  • Computational Biology

Background:

  • The cornea is vital for vision, and its diseases, like keratoconus, significantly impact ocular health.
  • Accurate diagnosis of corneal conditions, particularly keratoconus, is essential for timely intervention and management.
  • Corneal topography is a key diagnostic tool, providing topographical corneal parameters.

Purpose of the Study:

  • To explore the anatomy and pathology of the cornea, focusing on keratoconus.
  • To investigate the utility of machine learning models for diagnosing keratoconus based on corneal topography.
  • To evaluate the effectiveness of Decision Tree and Nearest Neighbor Analysis in classifying and predicting keratoconus.

Main Methods:

  • Review of corneal anatomy, pathology, and diagnostic techniques.
  • Application of Decision Tree and Nearest Neighbor Analysis to topographical corneal parameters.
  • Analysis of classification accuracy for training, testing, and holdout samples.

Main Results:

  • Decision Tree achieved 62% training and 65.7% testing accuracy.
  • Nearest Neighbor Analysis achieved 65.4% training and 62.6% holdout accuracy.
  • Both models demonstrated effectiveness in classifying and predicting conditions based on corneal parameters.

Conclusions:

  • Machine learning models, specifically Decision Tree and Nearest Neighbor Analysis, enhance the accuracy of keratoconus diagnosis.
  • These models provide valuable insights into disease progression and severity.
  • Integration of these technologies aids clinicians in treatment and management decisions for keratoconus.