Investigation of diabetic microvascular complications using data mining techniques

This study theoretically analyzes and numerically explores the relationship between the physiological data and three diabetic microvascular complications: diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy (foot problem). Method: The analysis results of 8,736 diabetic patients in northern Taiwan by using two data mining models: C5.0 and neural network were presented and compared. Results: It is found that Creatinine is the most important predictor for diabetic retinopathy. If Creatinine is out of control, diabetic patients will easily suffer from diabetic retinopathy in spite of many other laboratory evaluations are normal. The sensitivity and specificity for diabetic retinopathy prediction using C5.0 are 58.62 and 74.73, and those using neural network are 59.48 and 99.86, respectively. In addition, diabetic nephropathy will happen when several laboratory evaluation values are worse than target values. Female diabetics with diabetic family history are easier to undergo this complication. The sensitivity and specificity for diabetic nephropathy prediction using C5.0 are 69.44 and 81.36, and those using neural network are 74.44 and 98.55, respectively. For diabetic neuropathy, female diabetics feature unqualified BMI, HbAlc and AC sugar, while male diabetics mostly have uncontrolled blood pressure. Besides, smoking diabetics are more difficult to avoid this complication. The sensitivity and specificity for diabetic foot problem prediction using C5.0 are 64.71 and 83.48, and those using neural network are 67.63 and 99.70, respectively.