International Journal of Computer Theory and Engineering

Abstract—Accurate and early prediction of Cardiovascular Disease (CVD) plays an important role in improving patient survival and enabling timely preventive or therapeutic interventions. In this study, we propose an enhanced Crow Search Algorithm (eCSA) that integrates feature selection and hyperparameter optimization into a unified framework to improve the performance of a Bidirectional Long Short-Term Memory (Bi-LSTM) based heart disease prediction network. By employing a hybrid binary-continuous search strategy, the proposed eCSA jointly identifies discriminative features and optimizes Bi-LSTM hyperparameters, resulting in improved predictive performance and enhanced feature-level interpretability. Experimental evaluations were conducted on two benchmark datasets, namely the Cleveland Heart Disease dataset and the Framingham Heart Study dataset, under multiple train-test split conditions (80:20 and 70:30). Comparative analysis against state-of-the-art approaches, including Stochastic Configuration Network–Deep Bidirectional Long Short-Term Memory (SCN-Deep Bi-LSTM), Fitness-based Horse Optimization–Bidirectional Long Short-Term Memory (FHO-Bi-LSTM), Quantum Hybrid Deep Network (QHDN), and Convolutional Transformer Network (CTN-Trans), demonstrated that the proposed eCSA-BiLSTM model consistently outperformed competing methods across multiple evaluation metrics. Performance was assessed using accuracy, macro-averaged F1-Score (Macro-F1), precision, recall, Matthews Correlation Coefficient (MCC), and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The proposed model achieved an accuracy of up to 0.95 on the Cleveland dataset and 0.91 on the Framingham dataset, while maintaining stable and consistent performance across stratified 5-fold cross-validation and repeated train–test split experiments.

Keywords—enhanced Crow Search Algorithm (CSA), heart disease prediction, hyperparameter tuning

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Cite: Narayan Jee, Gesu Thakur, and Sumit Kumar, "An Enhanced Crow Search-optimized Bi-LSTM for Heart Disease Prediction: Hybrid Feature Selection and Hyperparameter Tuning," International Journal of Computer Theory and Engineering, vol. 18, no. 3, pp. 180-192, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).