# Predator crow search optimization with explainable AI for cardiac vascular disease classification

## TL;DR

A new AI framework improves cardiac disease prediction accuracy and explainability using optimized models and advanced segmentation techniques.

## Contribution

Introduces a novel optimization method combining Predator crow search with explainable AI for improved cardiovascular disease classification.

## Key findings

- Achieved 99.72% accuracy and 98.6% recall in cardiac vascular disease classification.
- Proposed an enhanced U-Net model for precise left ventricular segmentation.
- Demonstrated superior performance over existing methods using two clinical datasets.

## Abstract

The proposed framework optimizes Explainable AI parameters, combining Predator crow search optimization to refine the predictive model’s performance. To prevent overfitting and enhance feature selection, an information acquisition-based technique is introduced, improving the model’s robustness and reliability. An enhanced U-Net model employing context-based partitioning is proposed for precise and automatic left ventricular segmentation, facilitating quantitative assessment. The methodology was validated using two datasets: the publicly available ACDC challenge dataset and the imATFIB dataset from internal clinical research, demonstrating significant improvements. The comparative analysis confirms the superiority of the proposed framework over existing cardiovascular disease prediction methods, achieving remarkable results of 99.72% accuracy, 96.47% precision, 98.6% recall, and 94.6% F1 measure. Additionally, qualitative analysis was performed to evaluate the interpretability and clinical relevance of the model’s predictions, ensuring that the outputs align with expert medical insights. This comprehensive approach not only advances the accuracy of CVD predictions but also provides a robust tool for medical professionals, potentially improving patient outcomes through early and precise diagnosis.