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

2013 ◽  
Vol 9 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Andrei V. Kelarev ◽  
Jemal Abawajy ◽  
Andrew Stranieri ◽  
Herbert F. Jelinek
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Autonomic Neuropathy ◽  
Wearable Sensors ◽  
Ensemble Methods ◽  
Cardiac Autonomic Neuropathy ◽  
Cardiac Complications ◽  
Complications Of Diabetes ◽  
Clinical Complications ◽  
Diabetes Patients

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.

scholarly journals Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2849
Author(s):  
Sungbum Jun
Keyword(s):  
Decision Tree ◽  
Evolutionary Algorithm ◽  
Decision Trees ◽  
Manufacturing Systems ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Industrial Internet ◽  
Tree Models ◽  
Real World Datasets

Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree’s performance while maintaining its interpretability, an evolutionary algorithm for discretization of multiple attributes, called Decision tree Improved by Multiple sPLits with Evolutionary algorithm for Discretization (DIMPLED), is proposed. The experimental results with two real-world datasets from sensors showed that the decision tree improved by DIMPLED outperformed the performances of single-decision-tree models (C4.5 and CART) that are widely used in practice, and it proved competitive compared to the ensemble methods, which have multiple decision trees. Even though the ensemble methods could produce slightly better performances, the proposed DIMPLED has a more interpretable structure, while maintaining an appropriate performance level.

scholarly journals Cardiac autonomic neuropathy in type 1 and type 2 diabetes patients

BMC Neurology ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Anca Moţăţăianu ◽  
Smaranda Maier ◽  
Zoltan Bajko ◽  
Septimiu Voidazan ◽  
Rodica Bălaşa ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Autonomic Neuropathy ◽  
Cardiac Autonomic Neuropathy ◽  
Diabetes Patients

scholarly journals USING ATTRIBUTE BEHAVIOR DIVERSITY TO BUILD ACCURATE DECISION TREE COMMITTEES FOR MICROARRAY DATA

2012 ◽  
Vol 10 (04) ◽  
pp. 1250005 ◽  
Author(s):  
QIAN HAN ◽  
GUOZHU DONG
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Microarray Data ◽  
Dna Microarrays ◽  
Ensemble Methods ◽  
Medical Studies ◽  
Improve Performance ◽  
Gene Chips ◽  
New Ideas ◽  
And Behavior

DNA microarrays (gene chips), frequently used in biological and medical studies, measure the expressions of thousands of genes per sample. Using microarray data to build accurate classifiers for diseases is an important task. This paper introduces an algorithm, called Committee of Decision Trees by Attribute Behavior Diversity (CABD), to build highly accurate ensembles of decision trees for such data. Since a committee's accuracy is greatly influenced by the diversity among its member classifiers, CABD uses two new ideas to "optimize" that diversity, namely (1) the concept of attribute behavior–based similarity between attributes, and (2) the concept of attribute usage diversity among trees. The ideas are effective for microarray data, since such data have many features and behavior similarity between genes can be high. Experiments on microarray data for six cancers show that CABD outperforms previous ensemble methods significantly and outperforms SVM, and show that the diversified features used by CABD's decision tree committee can be used to improve performance of other classifiers such as SVM. CABD has potential for other high-dimensional data, and its ideas may apply to ensembles of other classifier types.

scholarly journals Identification and performance of multiple clinical and laboratorial risk factors for diagnosis of cardiac autonomic neuropathy in type 1 diabetes patients

2019 ◽  
Vol 18 (2) ◽  
pp. 565-573
Author(s):  
Cinthia Minatel Riguetto ◽  
Caroline Rigoleto Takano ◽  
Sharon Nina Admoni ◽  
Maria Candida Ribeiro Parisi ◽  
Maria Lucia Correa Giannella ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 1 Diabetes ◽  
Autonomic Neuropathy ◽  
Cardiac Autonomic Neuropathy ◽  
And Performance ◽  
Diabetes Patients

Automated Development of Clinical Strategies Using Multistage Decision Analysis

1986 ◽  
Vol 25 (04) ◽  
pp. 207-214 ◽  
Author(s):  
P. Glasziou
Keyword(s):  
Decision Tree ◽  
Decision Analysis ◽  
Decision Trees ◽  
Optimal Strategy ◽  
Cholestatic Jaundice ◽  
Clinical Strategies ◽  
Simultaneous Study

SummaryThe development of investigative strategies by decision analysis has been achieved by explicitly drawing the decision tree, either by hand or on computer. This paper discusses the feasibility of automatically generating and analysing decision trees from a description of the investigations and the treatment problem. The investigation of cholestatic jaundice is used to illustrate the technique.Methods to decrease the number of calculations required are presented. It is shown that this method makes practical the simultaneous study of at least half a dozen investigations. However, some new problems arise due to the possible complexity of the resulting optimal strategy. If protocol errors and delays due to testing are considered, simpler strategies become desirable. Generation and assessment of these simpler strategies are discussed with examples.

548-P: Early Liver Dysfunction Predicts Cardiac Autonomic Neuropathy in Adolescents with Type 1 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 548-P
Author(s):  
VALLIMAYIL VELAYUTHAM ◽  
PAUL Z. BENITEZ-AGUIRRE ◽  
MARIA E. CRAIG ◽  
JANINE CUSUMANO ◽  
GERALD LIEW ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Autonomic Neuropathy ◽  
Liver Dysfunction ◽  
Cardiac Autonomic Neuropathy

scholarly journals Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China

2021 ◽  
Vol 13 (2) ◽  
pp. 238
Author(s):  
Zhice Fang ◽  
Yi Wang ◽  
Gonghao Duan ◽  
Ling Peng
Keyword(s):  
Decision Trees ◽  
Landslide Susceptibility ◽  
Ensemble Methods ◽  
Landslide Susceptibility Mapping ◽  
Three Gorges Reservoir Area ◽  
Ratio Method ◽  
Susceptibility Map ◽  
Rotation Forest ◽  
Predictive Values ◽  
Ensemble Technique

This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning factors are selected and screened by the gain ratio method. Next, several training subsets are produced from the training set and a series of trained DTs are obtained by using a DT as a base classifier couple with different training subsets. Finally, the resultant landslide susceptibility map is produced by combining all the DT classification results using the RF ensemble technique. Experimental results demonstrate that the performance of all the DTs can be effectively improved by integrating them with the RF ensemble technique. Specifically, the proposed ensemble methods achieved the predictive values of 0.012–0.121 higher than the DTs in terms of area under the curve (AUC). Furthermore, the proposed ensemble methods are better than the most popular ensemble methods with the predictive values of 0.005–0.083 in terms of AUC. Therefore, the proposed ensemble framework is effective to further improve the spatial prediction of landslides.

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