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An Enhanced Classification Framework for Limited IoHT Time Series Data Using Ensemble Deep Learning and Image

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    This study introduces an Ensemble Deep Learning (DL) model for accurate time series classification with limited healthcare data. The novel approach effectively detects disorders using transformed time series, outperforming existing methods on the ECG5000 dataset.

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    Area of Science:

    • Medical Informatics
    • Artificial Intelligence in Healthcare
    • Time Series Analysis

    Background:

    • Deep Learning (DL) and Internet of Health Things (IoHT) show promise for disease detection using time series data.
    • Effective DL architectures for time series classification with limited data, especially for rare diseases, are underdeveloped.
    • A critical need exists for robust DL models capable of handling sparse clinical time series data.

    Purpose of the Study:

    • To investigate the efficacy of an Ensemble DL architecture for accurate time series classification using limited datasets.
    • To address the gap in DL models for time series classification in healthcare, particularly for rare diseases.
    • To develop and evaluate a novel framework combining CNN, ResNet, and MobileNet for enhanced predictive accuracy.

    Main Methods:

    • An Ensemble DL architecture was developed, integrating a deep Convolutional Neural Network (CNN) with transfer learning models (ResNet, MobileNet).
    • Time series data were transformed into 3D images using Recurrence Plot (RP), Gramian Angular Field (GAF), and Fuzzy Recurrence Plot (FRP).
    • The ensemble model was trained and evaluated on the ECG5000 dataset, focusing on performance with limited data.

    Main Results:

    • The proposed ensemble DL model demonstrated promising classification accuracy on a small dataset.
    • The method surpassed the performance of other state-of-the-art techniques on the ECG5000 dataset.
    • The architecture proved effective in handling limited clinical time series data.

    Conclusions:

    • The developed Ensemble DL architecture offers a robust solution for time series classification with limited clinical data.
    • Accurate predictions are achievable even with smaller datasets by employing this advanced DL framework.
    • This approach has significant clinical relevance for developing reliable models for rare diseases and other conditions with sparse data.