Key result
A proposed artificial intelligence-based framework automates the diagnosis of heart disease through preprocessing, feature extraction, and classification of ECG signals.
Why the study?
Heart disease is a leading cause of global mortality requiring early and precise diagnosis, and advances in machine learning offer avenues for automated ECG analysis to assist clinical decision-making.
Answers are grounded in this paper and cite their sources.
The paper proposes a machine learning framework for automated ECG analysis to assist in the early and precise diagnosis of heart disease.
Heart disease remains a leading cause of mortality worldwide. Early identification and precise diagnosis are essential for timely intervention and improved patient outcomes. A common diagnostic method for evaluating the condition of the heart is Electrocardiography (ECG). In recent years, advancements in machine learning and artificial intelligence techniques have paved the way for automated analysis of ECG data, providing promising avenues for efficient heart disease detection. This abstract presents a new method of detecting heart problems utilizing ECG data and machine learning algorithms. The system being proposed aims to automate the diagnosis process and assist healthcare professionals in making accurate and timely decisions. The framework contains three essential steps: Preprocessing, feature extraction, and classification of ECG signals.
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Balipa et al. (2024) studied Heart disease. Artificial Intelligence based ECG Analysis was evaluated. A proposed artificial intelligence-based framework automates the diagnosis of heart disease through preprocessing, feature extraction, and classification of ECG signals.
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