Diagnosis of type 2 diabetes using cost-sensitive learning

Diabetes is the fourth cause of death in the world and has some complications such as amputation, visual impairment, kidney disorder and early death. 80% of diabetes symptoms is avoidable by early diagnosis. There are standard methods to diagnose diabetes by measuring the plasma glucose concentration. However, screening all people is impossible due to financial shortages especially in developing countries. Therefore, it is proposed that the people more than 20 years old who are prone to diabetes be tested. Identifying diabetic people is possible by using different methods including machine learning algorithms. Standard machines assume balance in data and use all the available and related features to achieve lower error rates. However, in medical applications misclassification cost should be minimized as misclassification costs for healthy and patient instances are different. In addition, we are facing imbalanced data in the most medical issues including diabetes diagnosis. The features will also have different knowledge levels and costs. As a result, cost-sensitive methods should be applied in such situations. In this paper, we present and compare different cost-sensitive learning methods for diagnosis of type 2 diabetes. For evaluation of the methods two different data sets are used, which one of them is data set of Tabriz, Iran and the other one is for Pima Indian data set. By defining different scenarios, we will show that none of the studied methods has absolute superiority over the other methods and the performance of algorithms will be different due to used data set and defined scenario.