Machine learning approaches for discerning intercorrelation of hematological parameters and glucose level for identification of diabetes mellitus.
BACKGROUND
The aim of this study is to explore the relationship between hematological parameters and glycemic status in the establishment of quantitative population-health relationship (QPHR) model for identifying individuals with or without diabetes mellitus (DM).


METHODS
A cross-sectional investigation of 190 participants residing in Nakhon Pathom, Thailand in January-March, 2013 was used in this study. Individuals were classified into 3 groups based on their blood glucose levels (normal, Pre-DM and DM). Hematological (white blood cell (WBC), red blood cell (RBC), hemoglobin (Hb) and hematocrite (Hct)) and glucose parameters were used as input variables while the glycemic status was used as output variable. Support vector machine (SVM) and artificial neural network (ANN) are machine learning approaches that were employed for identifying the glycemic status while association analysis (AA) was utilized in discovery of health parameters that frequently occur together.


RESULTS
Relationship amongst hematological parameters and glucose level indicated that the glycemic status (normal, Pre-DM and DM) was well correlated with WBC, RBC, Hb and Hct. SVM and ANN achieved accuracy of more than 98 % in classifying the glycemic status. Furthermore, AA analysis provided association rules for defining individuals with or without DM. Interestingly, rules for the Pre-DM group are associated with high levels of WBC, RBC, Hb and Hct. Conclusion This study presents the utilization of machine learning approaches for identification of DM status as well as in the discovery of frequently occurring parameters. Such predictive models provided high classification accuracy as well as pertinent rules in defining DM.
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Apilak
A
Worachartcheewan
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand; Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Chanin
C
Nantasenamat
Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Pisit
P
Prasertsrithong
Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Jakraphob
J
Amranan
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Teerawat
T
Monnor
International Center for Medical and Radiological Technology, Golden Jubilee Medical Center, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Tassaneya
T
Chaisatit
International Center for Medical and Radiological Technology, Golden Jubilee Medical Center, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Wilairat
W
Nuchpramool
Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Virapong
V
Prachayasittikul
QPHR; data mining; diabetes mellitus; glucose; hematologic parameters; quantitative population-health relationship