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Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients

G. Parthiban, S. K. Srivatsa Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
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  1. G Parthiban and S K Srivatsa. Article: Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients. International Journal of Applied Information Systems 3(7):25-30, August 2012. BibTeX

    	author = "G. Parthiban and S. K. Srivatsa",
    	title = "Article: Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 3,
    	number = 7,
    	pages = "25-30",
    	month = "August",
    	note = "Published by Foundation of Computer Science, New York, USA"


Classifying data is a common task in Machine learning. Data mining plays an essential role for extracting knowledge from large databases from enterprises operational databases. Data mining in health care is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Most data mining methods depend on a set of features that define the behaviour of the learning algorithm and directly or indirectly influence the complexity of resulting models. Heart disease is the leading cause of death in the world over the past 10 years. Researches have been using several data mining techniques in the diagnosis of heart disease. Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin, or when the body cannot effectively use the insulin it produces. Most of these systems have successfully employed Machine learning methods such as Naïve Bayes and Support Vector Machines for the classification purpose. Support vector machines are a modern technique in the field of machine learning and have been successfully used in different fields of application. Using diabetics' diagnosis, the system exhibited good accuracy and predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.


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Data Mining, Diabetes, Heart Disease, Machine Learning Methods, Naïve Bayes Method and Support Vector Machines