An intelligent Medical Cyber–Physical System to support heart valve disease screening and diagnosis

Tartarisco, Gennaro; Cicceri, Giovanni; Bruschetta, Roberta; Tonacci, Alessandro; Campisi, Simona; Vitabile, Salvatore; Cerasa, Antonio; Distefano, Salvatore; Pellegrino, Alessio; Modesti, Pietro Amedeo; Pioggia, Giovanni

doi:10.1016/j.eswa.2023.121772

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…Currently, MLOps workflows are primarily deployed in industry applications, such as automated defect inspection in factory settings [ 45 ]. Tartaisco et al [ 46 ] have prototyped a cloud-based machine continual learning framework for automated detection of valvular disease using heart sounds. Our work demonstrates a framework for MLOps practices and a data-centric approach for identifying areas for iterative model improvement in medical image classification.…”

Section: Discussionmentioning

confidence: 99%

Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification

Wu,

Smith,

VanBerlo

et al. 2024

Diagnostics

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Deep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the lung sliding artifact on lung ultrasound (LUS), we pursued a validation strategy using external LUS data. As annotated LUS data are relatively scarce—compared to other medical imaging data—we adopted a novel technique to optimize the use of limited external data to improve model generalizability. Externally acquired LUS data from three tertiary care centers, totaling 641 clips from 238 patients, were used to assess the baseline generalizability of our lung sliding model. We then employed our novel Threshold-Aware Accumulative Fine-Tuning (TAAFT) method to fine-tune the baseline model and determine the minimum amount of data required to achieve predefined performance goals. A subgroup analysis was also performed and Grad-CAM++ explanations were examined. The final model was fine-tuned on one-third of the external dataset to achieve 0.917 sensitivity, 0.817 specificity, and 0.920 area under the receiver operator characteristic curve (AUC) on the external validation dataset, exceeding our predefined performance goals. Subgroup analyses identified LUS characteristics that most greatly challenged the model’s performance. Grad-CAM++ saliency maps highlighted clinically relevant regions on M-mode images. We report a multicenter study that exploits limited available external data to improve the generalizability and performance of our lung sliding model while identifying poorly performing subgroups to inform future iterative improvements. This approach may contribute to efficiencies for DL researchers working with smaller quantities of external validation data.

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Section: Discussionmentioning

confidence: 99%

Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification

Wu,

Smith,

VanBerlo

et al. 2024

Diagnostics

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“…Attribute culling factors β in feature space 0.1, 0.2, 0.3, 0.4, 0.5 The number of subsets of feature subspace 2α, α ∈ [1, 2, 3] The number of classifiers required for ensemble 2α, α ∈ [1, 2,3] Among them, β controls the feature reconstruction space's degree of importance and scale. The subset of feature subspace division and the number of classifiers required for ensemble control the size of the ensemble model.…”

Section: Parameters Rangementioning

confidence: 99%

“…In financial analysis [2], the basic features of each stock include opening price, closing price, high price, low price, volume, etc., and also have features such as macroeconomic indicators, company fundamental information, etc., which are used to construct complex stock market classification prediction models. Multiple data such as physiological signal data (heart rate, blood pressure, blood glucose level), electronic health records, diagnostic records, medication usage records, etc., are involved in the health and disease screening task [3], and data such as air quality sensor data, water quality monitoring data, meteorological data, and satellite remote sensing data are acquired through multiple sensors in an environmental monitoring [4] classification task. How to fully extract valid and interpretable information from these high-dimensional data has become a hot topic widely studied by scholars nowadays.…”

Section: Introductionmentioning

confidence: 99%

High-Dimensional Ensemble Learning Classification: An Ensemble Learning Classification Algorithm Based on High-Dimensional Feature Space Reconstruction

Zhao,

2024

Applied Sciences

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When performing classification tasks on high-dimensional data, traditional machine learning algorithms often fail to filter out valid information in the features adequately, leading to low levels of classification accuracy. Therefore, this paper explores the high-dimensional data from both the data feature dimension and the model ensemble dimension. We propose a high-dimensional ensemble learning classification algorithm focusing on feature space reconstruction and classifier ensemble, called the HDELC algorithm. First, the algorithm considers feature space reconstruction and then generates a feature space reconstruction matrix. It effectively achieves feature selection and reconstruction for high-dimensional data. An optimal feature space is generated for the subsequent ensemble of the classifier, which enhances the representativeness of the feature space. Second, we recursively determine the number of classifiers and the number of feature subspaces in the ensemble model. Different classifiers in the ensemble system are assigned mutually exclusive non-intersecting feature subspaces for model training. The experimental results show that the HDELC algorithm has advantages compared with most high-dimensional datasets due to its more efficient feature space ensemble capability and relatively reliable ensemble operation performance. The HDELC algorithm makes it possible to solve the classification problem for high-dimensional data effectively and has vital research and application value.

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“…By investigating the effectiveness of hybrid models combining different techniques, various researchers have explored diverse methodologies, including neural networks and various machine learning methods, to enhance prediction accuracy [3][4][5][6][7][8][9][10][11][12]. While these studies provide valuable insights, the variability in datasets, models, and outcomes underscores the complexity of the predictive task.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Ogunpola,

Saeed,

Basurra

et al. 2024

Diagnostics

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Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study’s primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study’s outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model’s diagnostic accuracy for heart disease.

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An intelligent Medical Cyber–Physical System to support heart valve disease screening and diagnosis

Cited by 8 publications

References 26 publications

Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification

Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification

High-Dimensional Ensemble Learning Classification: An Ensemble Learning Classification Algorithm Based on High-Dimensional Feature Space Reconstruction

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

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