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Background Currently 8.8% of the World's population aged from 20 to 79 have diabetes mellitus (DM); of this total is estimated that 50% have not been diagnosed and do not know they have the disease. The most common laboratory tests used for diagnosis include blood glucose (FPG) and glycated haemoglobin (HbA1c). The HbA1c test has advantages over FPG, therefore being recommended in diagnoses of DM. Early diagnoses are essential to prevent complications caused by DM; however, the symptoms of the initial stage are present in only 40% of the carriers, symptomless carriers oddly pursue the DM test. In a lifetime patients performs a series of laboratory exams for health analysis which is stored as laboratory data, the computational approach offers enormous potential in health data analysis discovering relevant results overlooked by physicians. Use machine learn approach on data stored of routine blood count laboratory tests to predict HbA1c diagnosis. Method: Using laboratory results from data stored of HbA1c, was formed six data groups composed of individuals: healthy and pre-diabetic (HP); healthy and diabetic (HD); pre-diabetic and diabetic (PD); healthy and non-healthy (HN); non-diabetic and diabetic (ND); and healthy, pre-diabetic and diabetic (HPD) patients. For each data group, was tested the K nearest neighbours (KNN), support vector machines (SVM), random forests (RF), naive Bayes (NB) and artificial neural network (ANN) models. Assessment of model performance was carried out using sensitivity, specificity, precision and negative prediction. Results The KNN model applied to the ND group had the best performance in the diagnosis of diabetes, resulting in a sensitivity value of 53.6% and an accuracy of 90.1%. The classification after regression with the neural network model (ANNr) and the ND group had a more general result, with a sensitivity of 74.3% and an accuracy of 77.2%. Analysing only the values for the regression, the neural network model presented a mean square error of 0.36 for the final test base with a correlation of 0.85. Conclusions We conclude that machine learning-based computational models can predict HbA1c values from other routine laboratory tests. Thus, they can assist in the detection of diabetes and act as a warning for undiagnosed cases. |
