DIAGNOSING DIABETES USING DATA MINING TECHNIQUES
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 0, Vol 4, Issue 11
Abstract
Diabetes Mellitus (DM) is an important health problem that affects many people including teenagers who get affected by it due to their sedentary lifestyle and food habits. Prevention or controlling diabetes is a big challenge as faced by diabetologists world over. Data mining is an important technique which can be used to find so far unpredicted pattern among huge heath care databases in a very effective and efficient manner. In fact, data mining is applied in many fields like education, retail, finance, healthcare, and many such fields. In this paper the rule based method named as PDC (Potential Diabetic Classifier) was used to diagnose diabetes from the healthcare database and it also diagnoses the type of diabetes from the diabetes dataset. Standard supervised machine learning algorithms like Naïve Bayes, SVM, Ada Boost, bagging, Decision Tree and Simple Cart were also used for diagnosing diabetes and also for predicting the type of diabetes like Gestational Diabetic Mellitus, Type-1 or Type-2 Diabetes. This paper compares the result of the standard classification methods with the PDC method and it was found that the PDC method gives better results when compared to other classification methods applied.
Authors and Affiliations
Srideivanai Nagarajan
Keywords
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