Cardiotech Next AI Healthcare: Enhanced Arrhythmia Cardio Vascular Disease Prediction using Hyper Capsule LSTM Gated Recurrent Neural Network with Optimal Swarm Intelligence Technique

The rising incidence of cardiovascular diseases, especially arrhythmias, demands advanced predictive models for early diagnosis and treatment. This study introduces the Cardiotech Next AI model, integrating Hyper Capsule Long Short-Term Memory (LSTM) and Gated Recurrent Neural Networks (GRNN) with an Optimal Swarm Intelligence Technique (OSIT) to enhance prediction accuracy. The process begins with C-score normalization to standardize cardiac data, followed by the Cardiac Disease Impact Behavioural Scaling Rate, which evaluates behavioral risk factors. OSIT-based feature selection optimizes predictor identification, improving model robustness and generalization. The final classification employs a Hyper Capsule LSTM-GRNN architecture to assign multiclass cardiovascular risk levels, leveraging temporal dependencies in cardiac data. The proposed system not only improves prediction performance but also enhances understanding of arrhythmia dynamics. Results demonstrate superior metrics precision (0.98), accuracy (0.81), recall (0.83), and F1-score (0.90), outperforming conventional models. This highlights the model's potential in advancing cardiovascular risk classification and supporting proactive healthcare management.