Authors :
Akazue Maureen; Ovoh Oghenefego; Abel E. Edje; Clement O. Ogeh; Hampo JohnPaul A.C
Volume/Issue :
Volume 8 - 2023, Issue 2 - February
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3Kftp7h
DOI :
https://doi.org/10.5281/zenodo.7649215
Abstract :
- 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
- 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
