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Diagnosis of Type 2 Diabetes and Pre-diabetes Using Machine Learning

Abstract:

The type-2 diabetes (T2D) is a multifactorial chronic disease that reduces the quality of lifestyle and produces the death of a large percentage of the population worldwide. Before the development of T2D a series of symptoms are presented even years before T2D diagnosis. This condition that appears before the development of T2D is called pbkp_rediabetes. Pbkp_rediabetes and T2D are diagnosed from the oral glucose tolerance test (OGTT). The OGTT consists in the measurement of glucose and insulin in five-time intervals, the first after 8 h of fasting (0 min) and the other four measurements after taking 75 g of oral glucose in 30-minutes intervals (30, 60, 90 and 120 min). Some parameters have been used to improve the efficiency in the diagnosis of pbkp_rediabetes and T2D, for example: the area under the glucose (AUCG) and insulin (AUCI) curve during OGTT has been used as a parameter for the diagnosis of pbkp_rediabetes, T2D and obesity. The aim of this study is to assess the k-means clustering algorithm in the classification of subjects with pbkp_rediabetes and T2D using the AUCG and AUCI. A database of 188 subjects (male = 88 subjects, age = 42.11 ± 14.91 years old) with values of plasma glucose and insulin during OGTT was used. The k-means clustering performed for AUCG presents acceptable results since the silhouette coefficient is above 0.6 in all cases. The findings in this study indicate that the k-means applied in the AUCG classify subjects with T2D, pbkp_rediabetes and control. Furthermore, it could even pbkp_redict those subjects with high probabilities of developing T2D.