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<p>The early detection and management of heart diseases have become increasingly feasible with the advent of artificial intelligence (AI)–driven biosensors. This research investigates the essential parameters and categories that underpin the effective performance of these biosensors. The analysis categorizes features into ECG-specific components, general physiological indicators, patient demographics, signal-processing techniques, computational models, and biomechanical parameters. ECG waveform features and heart-rate variability are particularly important, emphasizing their role in identifying cardiac irregularities. General physiological indicators, combined with vital signs and relevant biomarkers, provide a comprehensive overview of health status, while patient demographics enable personalized diagnostic and treatment strategies. Computational methods—especially machine learning (ML) algorithms such as the advanced stacking model, random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and decision tree—further enhance predictive diagnostics. The integration of neurological, mechanical, and optical signals expands the available data spectrum, supporting a holistic approach to cardiovascular health monitoring. This multidisciplinary framework, leveraging diverse data sources and advanced computational techniques, positions AI-based biosensors as a pivotal innovation in modern cardiovascular care. In this study, several machine-learning models were implemented, with the stacking model achieving an accuracy of 96%. In addition to accuracy, the study also reports performance metrics such as the F1-score, recall, precision, and confusion matrix. The potential for early detection and proactive management of heart diseases underscores the importance of continued research and development in this rapidly evolving field.</p>

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AI-driven biosensors for cardiac care: parametric-tuned modeling with extensive literature review

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Himadri Shekhar Mondal,
  • Yiwei Feng,
  • Owana Marzia Moushi,
  • Nick Birbilis

摘要

The early detection and management of heart diseases have become increasingly feasible with the advent of artificial intelligence (AI)–driven biosensors. This research investigates the essential parameters and categories that underpin the effective performance of these biosensors. The analysis categorizes features into ECG-specific components, general physiological indicators, patient demographics, signal-processing techniques, computational models, and biomechanical parameters. ECG waveform features and heart-rate variability are particularly important, emphasizing their role in identifying cardiac irregularities. General physiological indicators, combined with vital signs and relevant biomarkers, provide a comprehensive overview of health status, while patient demographics enable personalized diagnostic and treatment strategies. Computational methods—especially machine learning (ML) algorithms such as the advanced stacking model, random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and decision tree—further enhance predictive diagnostics. The integration of neurological, mechanical, and optical signals expands the available data spectrum, supporting a holistic approach to cardiovascular health monitoring. This multidisciplinary framework, leveraging diverse data sources and advanced computational techniques, positions AI-based biosensors as a pivotal innovation in modern cardiovascular care. In this study, several machine-learning models were implemented, with the stacking model achieving an accuracy of 96%. In addition to accuracy, the study also reports performance metrics such as the F1-score, recall, precision, and confusion matrix. The potential for early detection and proactive management of heart diseases underscores the importance of continued research and development in this rapidly evolving field.