Abstract:
In the data mining area, the prophecy of human diseases initiates a research zone for
researchers by applying various machine learning algorithms with various patterns. As a
modern community disease, diabetes is becoming one of the fastest-progressive human
diseases in the world because of eating heavily sugared foods and lack of proper diet
knowledge. In this era, most of the middle age people have confusion about the presence
of diabetes in their bodies. That’s why we choose to do research on diabetes. In this
research, we scrutinized the classification performance of six Meta Classifiers named as
Multiclass Classifier Updatable, Attribute Selected Classifier, Ada Boost M1, Logit
Boost, Bagging, and Filtered Classifier for forecasting diabetes through cross-validation
and percentage split techniques using in WEKA whereas as a diabetes dataset we used
Pima Indians Database. And finally, according to win-rate from the Win-Draw-Loss
table, the highest performance comes from Multiclass Classifier Updatable which has an
80% win-rate. On the other hand, in the measurement of highest individual accuracy,
81.9923% comes from both Attribute Selected Classifier and Filtered Classifier.
According to the measurement of the highest average performance, 66% Split as a
percentage split technique and Attribute Selected Classifier show the highest
performance.
