Tag: cardiovascular care

Performance Comparison of Machine Learning Classifiers and Cardio-Sense Ensemble for Heart Disease Detection

Announcing a new article publication for Cardiovascular Innovations and Applications journal. Heart disease remains a leading cause of global mortality; consequently, accurate, reliable, and interpretable predictive models are needed for early diagnosis. This study was aimed at developing a robust hybrid ensemble learning framework that improves heart disease predictive accuracy while preserving clinical interpretability.

The Cardio-Sense Ensemble Framework (CSEF) for heart disease prediction was developed by using the publicly available Behavioral Risk Factor Surveillance System dataset. Laplacian binary optimization was used for optimized feature selection, to eliminate redundancy and enhance discriminative information. The refined feature set was used to train multiple baseline classifiers, including logistic regression, random forest, (more…)

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LLM-Based Multimodal Planning for TAVR: Independent Validation, Calibration, and Efficiency Gains

TAVR, multimodal LLMAnnouncing a new article publication for Cardiovascular Innovations and Applications journal. A new study demonstrates that a multimodal large language model (LLM) can accurately support preoperative planning for transcatheter aortic valve replacement (TAVR). By integrating CT imaging and clinical data, the system automatically generates structured surgical plans, including valve selection, access strategy, and risk alerts. Tested across 950 patients, the model showed high agreement with expert heart teams while reducing planning time by over 90%. The findings highlight the potential of LLM-driven tools to improve efficiency and decision-making in complex cardiac procedures. (more…)

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