A Robust Heart Disease Prediction System Using Hybrid Deep Neural Networks

Reshan, Mana Saleh Al; Amin, Samina; Zeb, Muhammad Ali; Sulaiman, Adel; Alshahrani, Hani; Shaikh, Asadullah

doi:10.1109/access.2023.3328909

Cited by 22 publications

(1 citation statement)

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“…The ability of CNNs to capture local important information in electronic health records and utilize it for accurate disease predictions and diagnoses makes them a powerful tool in the development of advanced diagnostic systems for MI in echocardiogram frames. These advanced deep learning techniques have the potential to revolutionize the field of MI diagnosis by providing more accurate and efficient detection and classification of MI in echocardiogram frames (Reshan et al, 2023) [5]. The proposed research aims to contribute to the field of MI diagnosis by utilizing advanced deep learning techniques, specifically CNNs, for accurate and efficient detection and classification of MI in echocardiogram frames.By leveraging the capabilities of advanced deep learning techniques, specifically CNNs, our research endeavors to enhance the accuracy and efficiency of MI detection and classification in echocardiogram frames, ultimately leading to improved patient outcomes and more effective medical interventions.…”

Section: Introductionmentioning

confidence: 99%

Detecting and Classifying Myocardial Infarction in Echocardiogram Frames With an Enhanced CNN Algorithm and ECV-3D Network

Deepika,

Jaisankar

2024

IEEE Access

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Myocardial infarction is a serious medical condition that requires prompt and accurate diagnosis for effective treatment. In this paper, we present a novel approach for detecting and classifying MI in echocardiogram frames using an enhanced CNN algorithm and ECV-3D network. The proposed method aims to improve the accuracy and efficiency of MI diagnosis by leveraging advanced deep learning techniques. Through extensive experimentation, we demonstrate the effectiveness of our approach in achieving high accuracy and robustness in MI detection and classification. The results indicate the potential of our method to aid in the early and precise diagnosis of MI, thereby contributing to improved patient outcomes and clinical decision-making. After conducting thorough experimentation, our proposed approach has achieved an impressive accuracy of 97.05% in the detection and classification of myocardial infarction in echocardiogram frames. This accomplishment showcases the robustness and reliability of our method, indicating its potential to significantly impact the accurate diagnosis of MI and subsequently improve patient outcomes. Furthermore, the area under the curve attained by our model is 0.82%, reaffirming the efficacy of the enhanced CNN algorithm and ECV-3D network in accurately detecting and classifying MI.It is noteworthy that all the parameters utilized in our approach have demonstrated a high level of accuracy, emphasizing the effectiveness of our deep learning techniques in enhancing the diagnostic process for MI. Moreover, the proposed method is capable of efficiently processing large volumes of echocardiogram frames, making it suitable for real-time clinical applications.

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Section: Introductionmentioning

confidence: 99%