TITLE:
Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis
AUTHORS:
S. Prema, P. Umamaheswari
KEYWORDS:
Classification Model, Disease Diagnosis, Rough Set Model, Feature Selection, Multitude Classifier, Mercer Naïve
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.6,
May
12,
2016
ABSTRACT: Classification model has
received great attention in any domain of research and also a reliable tool for
medical disease diagnosis. The domain of classification model is used in
disease diagnosis, disease prediction, bio informatics, crime prediction and so
on. However, an efficient disease diagnosis model was compromised the disease
prediction. In this paper, a Rough Set Rule-based Multitude Classifier (RS-RMC)
is developed to improve the disease prediction rate and enhance the class
accuracy of disease being diagnosed. The RS-RMC involves two steps. Initially,
a Rough Set model is used for Feature Selection aiming at minimizing the
execution time for obtaining the disease feature set. A Multitude Classifier
model is presented in second step for detection of heart disease and for
efficient classification. The Na?ve Bayes Classifier algorithm is designed for
efficient identification of classes to measure the relationship between disease
features and improving disease prediction rate. Experimental analysis shows
that RS-RMC is used to reduce the execution time for extracting the disease
feature with minimum false positive rate compared to the state-of-the-art
works.