Abstract: Heart disease is one of the most common causes of illness and mortality worldwide, along with other cardiovascular disorders. Early detection and diagnosis of heart disease are crucial for preventing serious complications and saving lives. The study described in this abstract, "Advanced Predictive Models for Early Heart Disease Detection: Harnessing Embedded Machine Learning," investigates cutting-edge machine learning methods incorporated within medical applications to improve the early identification of heart disease. The study focuses on the employment of embedded machine learning algorithms in a broad framework created to analyse many health-related data sources, including electronic health records, wearable device data, and medical imaging. These artificial intelligence (AI) models are integrated into the healthcare infrastructure to provide real-time data analysis and prediction, enabling the early detection of those at risk for heart disease. The study emphasises the important benefits of embedded machine learning, including scalability, real-time tracking, and seamless integration with healthcare systems. Additionally, it talks on the difficulties with data privacy, data quality, and model interpretability when applied to embedded machine learning for the early diagnosis of heart disease. The findings of this study show the potential to fundamentally alter the prognosis of heart disease, thereby easing the burden of this serious health problem. This ground-breaking method provides the path for the creation of more individualised, precise, and effective instruments for the early detection and control of cardiac disease.
Keywords: Heart disease, Predictive models, Early detection, Embedded machine learning, Healthcare technology, Cardiovascular risk assessment
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DOI:
10.17148/IJARCCE.2024.13497
