Feature selection and classification model construction on type 2 diabetic patients’ data

An Early Detection Method of Type-2 Diabetes Mellitus in Public Hospital

“…Surprisingly, IBk with k=1 and J48 have worst performance than naive Bayes, whereas IBk and J48 have the same performance with accuracy 95,34% and 95,45%, respectively. These result also opposses to Huang [7] that stated J48 achieved best performace among IBk and naive Bayes. …”

“…Fourth, to place our result in the context with the study of Huang [7], they did not report ROC analysis and areas under ROC curves, but we present overall performance classifiers with ROC curves and area under our ROC curves.…”

“…In our study, plasmainsulin is the most important attribute since it has highest InfoGain. This method opposes to Huang [7] which use feature selection via model construction as rank method and attribute age become a major attribute.…”

“…Recently, Huang et al used complementary of three classification techniques such as Naive Bayes, C4.5, and IB1 can be found in [7]. The authors collected 3857 patients, described by 410 features.…”

“…Intelligent data analysis such as data mining was deployed in order to support the creation of knowledge to help clinicians in making decisions. The role of data mining is to extract interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from large amounts of data, in such a way that they can be put to use in areas such as decision support, prediction and estimation [7].…”