Current Advancements in Cardiovascular Disease Management using Artificial Intelligence and Machine Learning Models: Current Scenario and Challenges

Bhadri, Karan; Karnik, Niharika; Dhatrak, Pankaj

doi:10.1109/icetet-sip-2254415.2022.9791776

Cited by 7 publications

(4 citation statements)

References 47 publications

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“…This research focused on the ML utility in selecting features for the improvement of new ML models and identifying individuals for whom an expensive treatment like PET may be beneficial. Overall, our results lend credibility to the notion that ML-based AI might be used for the vitally crucial tasks of risk classification and MACE prediction [23]. Practitioners may be capable of making improved judgments when screening individuals with suspected or confirmed ischemic if the process of detecting ischemic is automated for risk prediction and enhancing patient choice for both surgical and non-surgical treatment options [24].…”

Section: Refinement Of Prognostic Estimates In Cardiac Ischemiasupporting

confidence: 66%

Untitled

2023

J. Med. Chem. Sci.

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Section: Refinement Of Prognostic Estimates In Cardiac Ischemiasupporting

confidence: 66%

Untitled

2023

J. Med. Chem. Sci.

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“…These result in uncertainty based on choosing the best technique that facilitates operation. Bias and interpretability errors are also limitations of computational intelligence techniques, which are not acceptable in the clinical domain [86].…”

Section: Challenges Of Computational Intelligence Techniques In Image...mentioning

confidence: 99%

Computational Intelligence Approaches for Enhancing Biomedical Image Processing Applications Based on Breast Cancer

Isa¹,

Iliyas²,

Zarma³

2024

Biomedical Engineering

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Recent advances in the cutting-edge technologies of biomedical sensing and image processing tools provide us with big data of biomedical and various types of images that can’t be processed within a finite period by professional clinicians. Various techniques for processing biomedical images comprise mathematical algorithms that extract vital diagnostic features from biomedical information and biological data. Because of the complexity and big size of the data computation, intelligence techniques have been applied in processing, visualizing, diagnostic, and classification tasks. This study will explore the effectiveness of the variously artificial intelligence approaches on biomedical signal and image processing applications. The researchers and community entirely will benefit from this study as a guide to the state-of-the-art artificial intelligence techniques for biomedical signal and image processing applications.

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“…Cardiovascular diseases (CVDs), responsible for a significant number of mortalities worldwide, underline the pressing need for accurate diagnostics and early detection [1]. Central to this goal is the Electrocardiogram (ECG), a non-invasive diagnostic tool proficient in identifying a variety of cardiac anomalies [2].…”

Section: Introductionmentioning

confidence: 99%

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach

Benchaira,

Bitam

2024

Biomed. Phys. Eng. Express

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Rapid and accurate electrocardiogram (ECG) signal classification is crucial in high-stakes healthcare settings. However, existing computational models often struggle to balance high performance with computational efficiency. This study introduces an innovative computational framework that combines transfer learning with traditional machine learning to optimize ECG classification. We use a pre-trained Stacked Convolutional Neural Network (SCNN) to generate high-dimensional feature embeddings, which are then evaluated by an array of machine learning classifiers. Our models demonstrate exceptional performance, particularly when utilizing embeddings from SCNNs trained on diverse datasets. This underscores the importance of data diversity in improving classifier discrimination. Notably, Multilayer Perceptrons (MLPs) stand out for their ability to balance computational efficiency with strong performance, achieving test F1-scores of 0.94 and 1.00 in multi-class and binary tasks on the CinC2017 dataset, and 0.85 and 0.99 on the CPSC2018 dataset. Our approach consistently outperforms existing methods, setting new benchmarks in ECG classification. The synergy between deep learning-based feature extraction and traditional machine learning through transfer learning offers a robust, efficient, and adaptable strategy for ECG classification, addressing a critical research gap and laying the groundwork for future advancements in this crucial healthcare field.

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Current Advancements in Cardiovascular Disease Management using Artificial Intelligence and Machine Learning Models: Current Scenario and Challenges

Cited by 7 publications

References 47 publications

Untitled

Untitled

Computational Intelligence Approaches for Enhancing Biomedical Image Processing Applications Based on Breast Cancer

Enhancing ECG signal classification through pre-trained stacked-CNN embeddings: a transfer learning approach

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