An Enhanced Model for the Prediction of Cataract Using Bagging Techniques

Authors : Akazue Maureen; Ovoh Oghenefego; Abel E. Edje; Clement O. Ogeh; Hampo JohnPaul A.C

Volume/Issue : Volume 8 - 2023, Issue 2 - February

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- A clouding of the human eye lens affecting vision is called a cataract; and this is the cause to most avoidable blindness in the world especially among the aged ones. Early treatment and surgery in any patience diagnosed of cataract prevents the wrong case of blindness and total vision impairment. A wrongly classified cataract is an issue that causes wrong treatment and waste of fund. These have become a problem in the medical field, even some opticians can’t swiftly detect and/or classify cataract. This research offers a solution through an enhanced model for the prediction of cataract. Bagging techniques of the ensemble algorithm of machine learning was applied in the development of this model. Bagging ensembled with KNN as the base estimator, was trained with dataset from MRL open website; and compared with some algorithms such as KNN, Navie Baye and Decision Tree. Bagging ensembled had the best accuracy as 82.66% for training set. The validation set and testing set has 82.78% and 82.88% accuracies respectively when bagging ensemble was used.

Keywords : Machine Learning, Ensembled Model, Human Cataract And Cataract Classification, Cataract Prediction, Bagging Technique


Paper Submission Last Date
29 - February - 2024

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In 1-2 Days

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