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An Efficient Approach to Predict Eye Diseases from Symptoms Using Machine Learning and Ranker-Based Feature Selection

Ahmed Al Marouf1,2, Md Mozaharul Mottalib3, Reda Alhajj1,4,5

  • 1Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada.

Bioengineering (Basel, Switzerland)
|January 21, 2023
PubMed
Summary

This study introduces a machine learning model to predict five common eye diseases, including cataracts and glaucoma, achieving high accuracy. The model aids in early diagnosis, potentially improving treatment outcomes for vision problems.

Keywords:
eye diseasemachine learningranker-based feature selectionsupport vector machinesymptomatic analysis

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Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Machine Learning

Background:

  • Eye diseases significantly impact human vision and quality of life.
  • Timely diagnosis and treatment are crucial for managing eye conditions, but expert access can be limited.
  • The high demand and cost of expert diagnosis often lead to delays or missed opportunities for intervention.

Purpose of the Study:

  • To develop an efficient machine learning model for predicting five common eye diseases.
  • To utilize ranker-based feature selection (r-FS) methods to enhance prediction accuracy.
  • To provide an automated diagnostic aid for conditions such as Cataracts (CT), Acute Angle-Closure Glaucoma (AACG), Primary Congenital Glaucoma (PCG), Exophthalmos or Bulging Eyes (BE), and Ocular Hypertension (OH).

Main Methods:

  • Employed efficient data collection and annotation by professional ophthalmologists.
  • Applied five distinct feature selection methods and two data splitting techniques (train-test, stratified k-fold cross-validation).
  • Evaluated nine machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), AdaBoost (AB), Logistic Regression (LR), k-Nearest Neighbour (k-NN), Bagging (Bg), Boosting (BS), and Support Vector Machine (SVM).

Main Results:

  • The Support Vector Machine (SVM) model achieved the highest accuracy of 99.11% using 10-fold cross-validation.
  • Logistic Regression (LR) demonstrated strong performance with 98.58% accuracy for an 80:20 train-test split ratio.
  • Comparative analysis utilized traditional performance metrics like accuracy, precision, sensitivity, and F1-Score.

Conclusions:

  • Machine learning models, particularly SVM and LR, show significant potential for accurate and early prediction of common eye diseases.
  • The proposed model, incorporating feature selection, can serve as a valuable tool to assist in the diagnosis of vision-threatening conditions.
  • Automated prediction systems can help overcome barriers to expert consultation, facilitating timely and effective treatment for eye diseases.