An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios

Su, Kaixiang; Wu, Jiao; Gu, Dongxiao; Yang, Shanlin; Deng, Shuyuan; Хакимова, Аида

doi:10.3390/diagnostics11122288

Cited by 6 publications

(2 citation statements)

References 51 publications

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“…In the study described in this paper, the ensemble learning approach had higher AUC and CV AUC scores, but the model without the ensemble approach had higher testing and average CV accuracy. Although studies that focused on the prediction of the risk for T2D reported improved results with the inclusion of ensemble learning methods, our results suggest that ensemble learning will not always yield higher metric scores [ 39 ].…”

Section: Discussionmentioning

confidence: 85%

Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis

Chang¹,

Fukuoka²,

Aouizerat³

et al. 2023

JMIR Diabetes

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Background Type 2 diabetes (T2D) has an immense disease burden, affecting millions of people worldwide and costing billions of dollars in treatment. As T2D is a multifactorial disease with both genetic and nongenetic influences, accurate risk assessments for patients are difficult to perform. Machine learning has served as a useful tool in T2D risk prediction, as it can analyze and detect patterns in large and complex data sets like that of RNA sequencing. However, before machine learning can be implemented, feature selection is a necessary step to reduce the dimensionality in high-dimensional data and optimize modeling results. Different combinations of feature selection methods and machine learning models have been used in studies reporting disease predictions and classifications with high accuracy. Objective The purpose of this study was to assess the use of feature selection and classification approaches that integrate different data types to predict weight loss for the prevention of T2D. Methods The data of 56 participants (ie, demographic and clinical factors, dietary scores, step counts, and transcriptomics) were obtained from a previously completed randomized clinical trial adaptation of the Diabetes Prevention Program study. Feature selection methods were used to select for subsets of transcripts to be used in the selected classification approaches: support vector machine, logistic regression, decision trees, random forest, and extremely randomized decision trees (extra-trees). Data types were included in different classification approaches in an additive manner to assess model performance for the prediction of weight loss. Results Average waist and hip circumference were found to be different between those who exhibited weight loss and those who did not exhibit weight loss (P=.02 and P=.04, respectively). The incorporation of dietary and step count data did not improve modeling performance compared to classifiers that included only demographic and clinical data. Optimal subsets of transcripts identified through feature selection yielded higher prediction accuracy than when all available transcripts were included. After comparison of different feature selection methods and classifiers, DESeq2 as a feature selection method and an extra-trees classifier with and without ensemble learning provided the most optimal results, as defined by differences in training and testing accuracy, cross-validated area under the curve, and other factors. We identified 5 genes in two or more of the feature selection subsets (ie, CDP-diacylglycerol-inositol 3-phosphatidyltransferase [CDIPT], mannose receptor C type 2 [MRC2], PAT1 homolog 2 [PATL2], regulatory factor X-associated ankyrin containing protein [RFXANK], and small ubiquitin like modifier 3 [SUMO3]). Conclusions Our results suggest that the inclusion of transcriptomic data in classification approaches for prediction has the potential to improve weight loss prediction models. Identification of which individuals are likely to respond to interventions for weight loss may help to prevent incident T2D. Out of the 5 genes identified as optimal predictors, 3 (ie, CDIPT, MRC2, and SUMO3) have been previously shown to be associated with T2D or obesity. Trial Registration ClinicalTrials.gov NCT02278939; https://clinicaltrials.gov/ct2/show/NCT02278939

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Section: Discussionmentioning

confidence: 85%

Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis

Chang¹,

Fukuoka²,

Aouizerat³

et al. 2023

JMIR Diabetes

View full text Add to dashboard Cite

show abstract

“…The proposed model determines the optimal number of layers and basic learners based on the distribution of data and characteristics of the diagnostic task, resulting in improved performance compared to other baseline models. This approach offers a novel solution for developing straightforward and interpretable machine learning models in computer-aided diagnosis tasks that involve diverse datasets and feature sets [10] augmentation, stacking, and bagging, on performance. The pipeline was tested on four medical imaging datasets and demonstrated that stacking achieved the highest performance boost, with up to a 13% rise in F1-score.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Retinal Scan Classification: A Comparative Study of Transfer Learning and Ensemble Techniques

Shitole,

Kenchappagol,

Jangle

et al. 2023

IJRITCC

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Ophthalmic diseases are a significant health concern globally, causing visual impairment and blindness in millions of people, particularly in dispersed populations. Among these diseases, retinal fundus diseases are a leading cause of irreversible vision loss, and early diagnosis and treatment can prevent this outcome. Retinal fundus scans have become an indispensable tool for doctors to diagnose multiple ocular diseases simultaneously. In this paper, the results of a variety of deep learning models (DenseNet-201, ResNet125V2, XceptionNet, EfficientNet-B7, MobileNetV2, and EfficientNetV2M) and ensemble learning approaches are presented, which can accurately detect 20 common fundus diseases by analyzing retinal fundus scan images. The proposed model is able to achieve a remarkable accuracy of 96.98% for risk classification and 76.92% for multi-disease detection, demonstrating its potential for use in clinical settings. By utilizing the proposed model, doctors can provide swift and accurate diagnoses to patients, improving their chances of receiving timely treatment and preserving their vision.

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Reassessing Addison’s: Engineering Errors in Diagnosis and Ranges

Biswas,

Yukta,

Khandelwal

et al. 2024

Lecture Notes in Networks and Systems

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An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios

Cited by 6 publications

References 51 publications

Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis

Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis

Enhancing Retinal Scan Classification: A Comparative Study of Transfer Learning and Ensemble Techniques

Reassessing Addison’s: Engineering Errors in Diagnosis and Ranges

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