Hybrid prediction model for Type-2 diabetic patients

• Published in: Expert systems with applications Vol. 37; no. 12; pp. 8102 - 8108
• Main Authors: Patil, B.M., Joshi, R.C., Toshniwal, Durga
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2010
• Subjects: Accuracy; Algorithms; Classification; Construction; Data mining; Diabetes; Hybrid Prediction Model; k-Fold cross-validation; K-means clustering; Mathematical models; Medical; Patients; Pima Indians diabetes; Pima Indians diabetes; Hybrid Prediction Model; k-Fold cross-validation; Data mining; K-means clustering; Classification
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2010.05.078

A wide range of computational methods and tools for data analysis are available. In this study we took advantage of those available technological advancements to develop prediction models for the prediction of a Type-2 Diabetic Patient. We aim to investigate how the diabetes incidents are affected by patients’ characteristics and measurements. Efficient predictive modeling is required for medical researchers and practitioners. This study proposes Hybrid Prediction Model (HPM) which uses Simple K-means clustering algorithm aimed at validating chosen class label of given data (incorrectly classified instances are removed, i.e. pattern extracted from original data) and subsequently applying the classification algorithm to the result set. C4.5 algorithm is used to build the final classifier model by using the k-fold cross-validation method. The Pima Indians diabetes data was obtained from the University of California at Irvine (UCI) machine learning repository datasets. A wide range of different classification methods have been applied previously by various researchers in order to find the best performing algorithm on this dataset. The accuracies achieved have been in the range of 59.4–84.05%. However the proposed HPM obtained a classification accuracy of 92.38%. In order to evaluate the performance of the proposed method, sensitivity and specificity performance measures that are used commonly in medical classification studies were used.