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Intelligent Naïve Bayes Approach to Diagnose Diabetes Type-2

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IJCA Special Issue on Issues and Challenges in Networking, Intelligence and Computing Technologies
© 2012 by IJCA Journal
ICNICT - Number 3
Year of Publication: 2012
Authors:
Abid Sarwar
Vinod Sharma

Abid Sarwar and Vinod Sharma. Article: Intelligent Naive Bayes Approach to Diagnose Diabetes Type-2. IJCA Special Issue on Issues and Challenges in Networking, Intelligence and Computing Technologies ICNICT(3):14-16, November 2012. Full text available. BibTeX

@article{key:article,
	author = {Abid Sarwar and Vinod Sharma},
	title = {Article: Intelligent Naive Bayes Approach to Diagnose Diabetes Type-2},
	journal = {IJCA Special Issue on Issues and Challenges in Networking, Intelligence and Computing Technologies},
	year = {2012},
	volume = {ICNICT},
	number = {3},
	pages = {14-16},
	month = {November},
	note = {Full text available}
}

Abstract

Artificial Intelligence is now a days gaining immense importance and is becoming a key technology in many fields ranging from banking industry, to travel industry, to communication industry, and to robotic industry. The use of Artificial Intelligence in medical diagnosis too is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, hepatitis, lung diseases, etc. . . The main aim of this paper is to build an Artificial Intelligent System that after analysis of certain parameters can predict that whether a person is diabetic or not. Diabetes is inability of body to manage the levels of sugar in the blood. It being one of the most chronic diseases around the world causes around 3. 8 million deaths every year. Authors have identified 10 parameters that play an important role in diabetes and prepared a rich database of training data which served as the backbone of the prediction algorithm. Keeping in view this training data authors developed a system that uses the naïve-Bayes classification algorithm to serve the purpose. When the parameters of the test data are fed to the system, it anticipates & classifies the test data into one of the two categories viz diabetic & not diabetic. The performance of AI method when compared with the medical diagnosis system was found to be 95%. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results.

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