Contextual Learning Approach to Improve Diagnostic Accuracy for Hybrid (Lung, HIV and Heart) Diseases

Author(s):

R. Delphine Clare , R.V.S Educational Trust's Group of Institutions; J. Avanija, R.V.S Educational Trust Group Of Institution

Keywords:

Contextual learning, Hybrid (Lung, HIV, Heart) diseases, Data Mining Algorithms, Computer Aided Diagnosis System, Hybrid disease prediction System

Abstract:

Clinicians need to routinely make management decisions about patients who are at risk for a disease such as Hybrid (Lung, HIV and Heart) diseases. Poor clinical decisions can lead to disastrous consequences which are therefore unacceptable. Hospitals must also minimize the cost of clinical tests. Quality service implies diagnosing patients correctly and administering treatments that are effective. This paper design a Hybrid diseases predict system (HDPS) with high accuracy using Neural Network, Naive Bayes, Decision tree, Linear Regression and EM Algorithm. Apply hybrid data mining techniques to Hybrid disease diagnosis benchmark dataset to establish baseline accuracy for each hybrid data mining technique in the diagnosis of Lung, heart and HIV disease patients.

Other Details:

Manuscript Id	:	IJSTEV2I11248
Published in	:	Volume : 2, Issue : 11
Publication Date	:	01/06/2016
Page(s)	:	585-588

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