Healthcare diagnostics with an adaptive deep learning model integrated with the Internet of medical Things (IoMT) for predicting heart disease

Baseer, K.K.; Sivakumar, K.; Veeraiah, Duggineni; Chhabra, Gunjan; Kumar Lakineni, Prasanna; Jahir Pasha, M.; Gandikota, Ramu; Harikrishnan, Gopakumar

doi:10.1016/j.bspc.2024.105988

Cited by 6 publications

References 20 publications

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A comprehensive review of deep learning-based models for heart disease prediction

Zhou,

Dai,

Hou

et al. 2024

Artif Intell Rev

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Heart disease (HD) is one of the leading causes of death in humans, posing a heavy burden on society, families, and patients. Real-time prediction of HD can reduce mortality rates and is crucial for timely intervention and treatment of HD. Deep learning (DL)related methods have higher accuracy and real-time performance in predicting HD. In this study, we comprehensively compared and evaluated the contributions and limitations of DL algorithms, extended deep learning (ETDL) algorithms, and integrated deep learning (integrated DL) algorithms that combine DL with other technologies for predicting HD. The articles considered span the period from 2018 to 2023, and after rigorous screening, 64 articles were selected for preliminary research. A systematic literature review of realtime HDP will provide future researchers with a comprehensive understanding of existing deep learning methods and related integrated technologies in the healthcare industry. Furthermore, it discusses popular datasets employed in deploying numerous prediction models. Additionally, it reveals existing open problems or challenges encountered by previous researchers. Notably, the most prevalent challenge is the scarcity of large discrete datasets, followed by the need for further improvement of existing models.

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A comprehensive review of deep learning-based models for heart disease prediction

Zhou,

Dai,

Hou

et al. 2024

Artif Intell Rev

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Advances in graphene-assisted flexible substrate sensors for human motion monitoring

2024

International Journal of Electrochemical Science

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Heart health prediction and classification: An IoMT and AI collaborative model

Sundar,

Gangopadhyay,

Gupta

et al. 2024

MATEC Web Conf.

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Internet of Things (IoT) technology has been used in medical care as the Internet of Medical Things (IoMT) to gather sensor data for diagnosing and predicting cardiac disease. IoMT allows users to access real-time tracking information and manually estimate the person's health using Machine Learning (ML) algorithms. The primary goal of the study proposal is to categorize data and forecast heart illness using health information and medical imagery. The suggested IoMT-based Heart Health Prediction and Classification (IoMT-HHPC) model is a medical data categorization and forecasting framework in two phases. If the first stage's outcome effectively predicts heart disease, the second step is image classification. Data collected from medical equipment attached to the person's body were initially categorized. Echocardiography (ECG) images were analyzed to forecast cardiac problems. This article used many ML techniques to forecast cardiac disease. An IoMT-HHPC model with ANN achieved an accuracy of 99.02%, surpassing the performance of other ML algorithms.

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Healthcare diagnostics with an adaptive deep learning model integrated with the Internet of medical Things (IoMT) for predicting heart disease

Cited by 6 publications

References 20 publications

A comprehensive review of deep learning-based models for heart disease prediction

A comprehensive review of deep learning-based models for heart disease prediction

Advances in graphene-assisted flexible substrate sensors for human motion monitoring

Heart health prediction and classification: An IoMT and AI collaborative model

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