Abstract
Cardiac autonomic neuropathy (CAN) is a well known complication of diabetes leading to impaired regulation of blood pressure and heart rate, and increases the risk of cardiac associated mortality of diabetes patients. The neurological diagnostics of CAN progression is an important problem that is being actively investigated. This paper uses data collected as part of a large and unique Diabetes Screening Complications Research Initiative (DiScRi) in Australia with data from numerous tests related to diabetes to classify CAN progression. The present paper is devoted to recent experimental investigations of the effectiveness of applications of decision trees, ensemble classifiers and multi-level ensemble classifiers for neurological diagnostics of CAN. We present the results of experiments comparing the effectiveness of ADTree, J48, NBTree, RandomTree, REPTree and SimpleCart decision tree classifiers. Our results show that SimpleCart was the most effective for the DiScRi data set in classifying CAN. We also investigated and compared the effectiveness of AdaBoost, Bagging, MultiBoost, Stacking, Decorate, Dagging, and Grading, based on Ripple Down Rules as examples of ensemble classifiers. Further, we investigated the effectiveness of these ensemble methods as a function of the base classifiers, and determined that Random Forest performed best as a base classifier, and AdaBoost, Bagging and Decorate achieved the best outcomes as meta-classifiers in this setting. Finally, we investigated the meta-classifiers that performed best in their ability to enhance the performance further within the framework of a multi-level classification paradigm. Experimental results show that the multi-level paradigm performed best when Bagging and Decorate were combined in the construction of a multi-level ensemble classifier.
Highlights
Neurological disorders often span multiple chronic disease entities such as diabetes, kidney and cardiovascular disease and present an area of medical practice where data mining can provide assistance in clinical decision making
A hybrid of Maximum Relevance filter (MR) and Artificial Neural Net Input Gain Measurement Approximation (ANNIGMA) wrapper approaches were used to reduce the number of features necessary for optimal classification
In medical applications it is important to consider models produced by the classifiers that can be expressed in a clear form and facilitate their application in clinical practice
Summary
Neurological disorders often span multiple chronic disease entities such as diabetes, kidney and cardiovascular disease and present an area of medical practice where data mining can provide assistance in clinical decision making. Decision making and diagnosis in medical practice is most often based on incomplete data due to either unavailability of diagnostic laboratory services, technical issues or lack of patient cooperation as well as counter-indications for undertaking certain diagnostic tests. Powerful decision rules can be determined, which enhance the diagnostic accuracy when an incomplete patient profile is available or multiclass presentations are possible. In order to reduce the cost of performing medical tests required to collect the attributes yet maintain diagnostic accuracy, it is essential to optimize the features used for classification and to keep the number of features as small as possible. 2. Cardiac Autonomic Neuropathy and DiScRi Dataset. Diabetes Mellitus Type II and Cardiac Autonomic Neuropathy
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