the medical community has been concerned
about how to increase the accuracy of different classification
methods with large data that are being generated every day.
The traditional KNN method has many limitations, such as
dealing with large-scale data, handling uncertain data, and also
determining the k parameter for KNN that gives the best
result. In this research, three limitations are solved. By using
optimized feature selection methods, optimal features are
chosen from many irrelevant features. Then, uncertain data
that has a conflict with the class label is handled. Finally, the
optimal number of k in the KNN method that gives better
accuracy is chosen using the ROC curve. The prime objective
of this paper is to develop a hybrid optimal model for medical
data classification that handles these challenges. The results
are evaluated using the accuracy metric. Experimental results
show that the enhanced KNN method outperforms the
previously used KNN method in used medical datasets