Empirical Investigation of Decision Tree Ensembles for Monitoring Cardiac Complications of Diabetes

Abstract

Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature.

Predicting cardiac autonomic neuropathy category for diabetic data with missing values

“…The article [11] dealt only with two classes of CAN, i.e., it handled only binary classifications. For the set of features considered in [11] the decision tree ensemble generated by Decorate based on RandomTree turned out the best.…”

“…For the set of features considered in [11] the decision tree ensemble generated by Decorate based on RandomTree turned out the best. It achieved the accuracy of 94.23%.…”

“…The accuracy obtained by the best classifier investigated in [10] is equal to 91.61%. Only two classes of CAN were considered in [14,28,12,13,11]. The accuracies of the best classifiers designed in [28,[12][13][14] are equal to 80.66%, 94.61%, 94.84%, 97.74%, respectively.…”

“…Algorithms for the classification of CAN data have been considered recently, for example, in [10][11][12][13][14][15][16][17]. All these articles investigated different classification schemes and applied one and the same general approach.…”

Ideal basis in constructions defined by directed graphs

“…[61], Section 7.5). Let us refer the readers to a few recent examples of papers in high quality journals devoted to the applications of classifiers in security [1,4,20,38,50] and health informatics [3,5,6,31,37,60]. The role of ideals with largest weight in the design of classifiers is very well explained in [2], where a nice diagram illustrating the classification process is given.…”