Diagnosing Diabetes Type II Using a Soft Intelligent Binary Classification Model

Mehdi Khashei ； Saeede Eftekhari ； Jamshid Parvizian

《Review of Bioinformatics and Biometrics》 1卷1期 (2012/12) Pp. 9-23

並列摘要

Diabetes, named also silent killer, is a metabolic disease characterized by high blood glucose levels, which result from body does not produce enough insulin or the body is resistant to the effects of insulin. Classification models are one of the most widely used groups of data mining tools that greatly help physicians to improve their prognosis, diagnosis or treatment planning procedures. Classification accuracy is one of the most important features in order to choose the appropriate classification model; hence, the researches directed at improving upon the effectiveness of these models have never stopped. Nowadays, despite the numerous classification models proposed in several past decades, it is widely recognized that diabetes are extremely difficult to classify. In this paper, a hybrid binary classification model is proposed for diabetes type II classification, based on the basic concepts of soft computing and artificial intelligence techniques. Empirical results of Pima Indian diabetes data classification indicate that hybrid model is generally better than other linear/nonlinear, soft/hard, and classic/intelligent classification models presented for diabetes classification. Therefore, our proposed model may be a suitable alternative model for medical classification to achieve greater accuracy, and to improve medical diagnosis.

並列關鍵字

Artificial Intelligence ； Soft Computing ； Classification ； Medical Diagnosis ； Diabetes