A hybrid intelligent system for diagnosing microalbuminuria in type 2 diabetes patients without having to measure urinary albumin

Microalbuminuria (MA) is an independent predictor of cardiovascular and renal disease, development of overt nephropathy, and cardiovascular mortality in patients with type 2 diabetes. Detecting MA is an important screening tool to identify people with high risk of cardiovascular and kidney disease. The gold standard to detect MA is measuring 24-h urine albumin excretion. A new method for MA diagnosis is presented in this manuscript which uses clinical parameters usually monitored in type 2 diabetic patients without the need of an additional measurement of urinary albumin. We designed an expert-based fuzzy MA classifier in which rule induction was performed by particle swarm optimization. A variety of classifiers was tested. Additionally, multiple logistic regression was used for statistical feature extraction. The significant features were age, diabetic duration, body mass index and HbA1C (the average level of blood sugar over the previous 3 months, which is routinely checked every 3 months for diabetic patients). The resulting classifier was tested on a sample size of 200 patients with type 2 diabetes in a cross-sectional study. The performance of the proposed classifier was assessed using (repeated) holdout and 10-fold cross-validation. The minimum sensitivity, specificity, precision and accuracy of the proposed fuzzy classifier system with feature extraction were 95%, 85%, 84% and 92%, respectively. The proposed hybrid intelligent system outperformed other tested classifiers and showed "almost perfect agreement" with the gold standard. This algorithm is a promising new tool for screening MA in type-2 diabetic patients.