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

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Author(s)Marouf, Ahmed Al
Author(s)Mottalib, Md Mozaharul
Author(s)Alhajj, Reda
Author(s)Rokne, Jon
Author(s)Jafarullah, Omar
Date Accessioned2023-03-09T20:09:12Z
Date Available2023-03-09T20:09:12Z
Publication Date2022-12-24
DescriptionThis article was originally published in Bioengineering. The version of record is available at: https://doi.org/10.3390/bioengineering10010025
AbstractThe eye is generally considered to be the most important sensory organ of humans. Diseases and other degenerative conditions of the eye are therefore of great concern as they affect the function of this vital organ. With proper early diagnosis by experts and with optimal use of medicines and surgical techniques, these diseases or conditions can in many cases be either cured or greatly mitigated. Experts that perform the diagnosis are in high demand and their services are expensive, hence the appropriate identification of the cause of vision problems is either postponed or not done at all such that corrective measures are either not done or done too late. An efficient model to predict eye diseases using machine learning (ML) and ranker-based feature selection (r-FS) methods is therefore proposed which will aid in obtaining a correct diagnosis. The aim of this model is to automatically predict one or more of five common eye diseases namely, Cataracts (CT), Acute Angle-Closure Glaucoma (AACG), Primary Congenital Glaucoma (PCG), Exophthalmos or Bulging Eyes (BE) and Ocular Hypertension (OH). We have used efficient data collection methods, data annotations by professional ophthalmologists, applied five different feature selection methods, two types of data splitting techniques (train-test and stratified k-fold cross validation), and applied nine ML methods for the overall prediction approach. While applying ML methods, we have chosen suitable classic ML methods, such as 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). We have performed a symptomatic analysis of the prominent symptoms of each of the five eye diseases. The results of the analysis and comparison between methods are shown separately. While comparing the methods, we have adopted traditional performance indices, such as accuracy, precision, sensitivity, F1-Score, etc. Finally, SVM outperformed other models obtaining the highest accuracy of 99.11% for 10-fold cross-validation and LR obtained 98.58% for the split ratio of 80:20.
SponsorThis research received no external funding.
CitationMarouf, Ahmed Al, Md Mozaharul Mottalib, Reda Alhajj, Jon Rokne, and Omar Jafarullah. 2023. "An Efficient Approach to Predict Eye Diseases from Symptoms Using Machine Learning and Ranker-Based Feature Selection Methods" Bioengineering 10, no. 1: 25. https://doi.org/10.3390/bioengineering10010025
ISSN2306-5354
URLhttps://udspace.udel.edu/handle/19716/32400
Languageen_US
PublisherBioengineering
Keywordseye disease
Keywordsmachine learning
Keywordsranker-based feature selection
Keywordssymptomatic analysis
Keywordssupport vector machine
TitleAn Efficient Approach to Predict Eye Diseases from Symptoms Using Machine Learning and Ranker-Based Feature Selection Methods
TypeArticle
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