Integrating Large-Scale Data Analytics for Cardiovascular Disease Prediction: A Scoping Review

Abstract

While machine learning provides considerable opportunities for predicting CVD outcomes, limitations remain. Machine-learning approaches are not always the most appropriate option, particularly in basic research where causal relationships between variables may be more critical than optimized predictions. To ensure fair and effective healthcare outcomes, issues related to bias, data quality, ethical concerns, and practical implementation must be addressed. Overcoming these challenges will require interdisciplinary collaboration, methodological refinement, and further research.

Ähnliche Arbeiten