Performance Analysis of Diabetes Disease using Classification Algorithms by WEKA
Keywords:
Diabetes, Health care, Naive Bayes, Bayes Net, J48 and Random Forest, WEKAAbstract
In Medical industry there are many diseases that makes a patient critical among them diabetes is one of the major disease that affect most of the people in early stage. Diabetes (or Diabetes Mellitus) is a group of metabolic diseases, chronic, in which there are high blood sugar levels and affects the body’s ability to use the energy found in food over a prolonged period. Researchers are finding effective methods for the prediction of diabetes. The main goal is to analysis the performance of various data mining techniques in the diabetes dataset for efficient extraction of valuable patterns. For doing so WEKA software was used as a mining tool for diagnosing the useful pattern. The Pima Indian diabetes dataset are used for the analysis. The dataset was applied in various classification algorithms to analysis the performance to identify an effective model that predict diabetes disease. In this, the analysis is done by applying attribute evaluator to enhance the accuracy then applying Naive Bayes, Bayes Net, J48 and Random Forest and the performance are compared. Through this study, Naive Bayes Algorithm provides better classification accuracy, when compared with classification algorithms like Bayes Net, J48 and Random Forest
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