Key result
Data-driven analysis of cardiac health markers revealed substantial demographic disparities in ECG abnormalities, weight distributions, and diagnostic frequencies to inform predictive models.
Observational
Data-driven analysis of cardiac health markers highlights demographic disparities, supporting the development of targeted predictive models for cardiovascular risk identification.
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Demographic disparities may inform targeted risk models; hypothesis-generating and should not yet change practice without prospective validation.
This study analyzes cardiac health markers across age groups and genders using data-driven methods to find patterns and connections in ECG abnormalities and other health parameters. The study quantifies demographic health inequalities using Z-score analysis, weight difference computations, and anomaly ratio assessments. Overall, summary data and pair plots reveal heart health patterns, including substantial disparities in ECG abnormalities, weight distributions, and diagnostic frequency- cies. The findings help create prediction models for heart disease identification and intervention, especially in vulnerable groups. This research affects individualized healthcare and targeted preventative interventions to improve patient outcomes using data.
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Mageswari et al. (2024) conducted an observational in Cardiovascular Risk. Data-driven predictive modeling was evaluated on Patterns and connections in ECG abnormalities and other health parameters. Data-driven analysis of cardiac health markers revealed substantial demographic disparities in ECG abnormalities, weight distributions, and diagnostic frequencies to inform predictive models.
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