AI-Driven Early Prediction of Sudden Cardiac Death Using Wearable Devices and Continuous Cardiovascular Monitoring

Abstract

Background: Sudden cardiac death (SCD) is one of the leading causes of death in the world and is usually unpredictable since it may occur due to unnoticed heart defects. The current ways of surveillance fall short of providing real-time analysis and forecasting of risks. Recent advances in Artificial Intelligence (AI) and wearable-enabled healthcare have offered a new arena of proactive cardiovascular monitoring and the first response.

Objective: The main goal of this study is to examine the potential of AI-based continuous cardiovascular monitoring for the early detection and prediction of sudden cardiac death. The level of public awareness, trust, acceptance and perception of healthcare technologies, i.e. AI-based and wearable monitoring devices, will also be taken into consideration in the study.

Methodology: This research study has adopted a quantitative research approach. Primary data was collected in a structured questionnaire comprising 287 respondents of different demographics. Statistical methods that included Normality Test, Reliability Test, Validity Test (KMO and Bartlett's Test), Independent Samples t-test, One-Way ANOVA, Kruskal-Wallis Test, Chi-Square Test of Independence, Pearson Correlation Analysis and Regression Analysis were used to analyze the collected data. The artificial intelligence variables, which comprised trust in AI systems, 24/7 cardiovascular surveillance, device wearing and the effectiveness of early detection, were used to quantify their impact on the prediction of sudden cardiac death.

Results: The analysis results indicated that the data collected was statistically normal, reliable and valid. The reliability analysis has given a great Cronbach's Alpha over 0.90, whereas testing of validity gave an impression that there was enough sampling and significant correlation among the variables. The outcome of testing the hypothesis demonstrated that there were significant relationships between demographic factors and acceptance of AI-based healthcare systems. Regression Analysis and Pearson Correlation indicated that the awareness of AI, a belief in the AI systems, use of the wearable technology, continuous monitoring and the effectiveness in early detection have strong positive correlations. AI accuracy has proved to be the most powerful predictor of better early sudden cardiac death prediction.

Conclusion: The paper will conclude with the idea that the potential of AI-based uninterrupted cardiovascular surveillance can be used to predict and prevent sudden cardiac death more effectively. Including wearable sensors, real-time monitoring of physiological processes and AI predictive algorithms can enhance the effectiveness of health care, induced on time medical care and reduce the risk of cardiac mortality. The findings indicate applications of smart technologies in heart monitoring in future preventive health care systems.