Integer Guided Linear Hopper Strategy in an LSTM Ensemble Framework for Prognosis Prediction
Parthiban M
Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi–NCR Campus, Ghaziabad, Uttar Pradesh, India.
Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi–NCR Campus, Ghaziabad, Uttar Pradesh, India.
Accurate and timely prognosis is essential for effective patient management and improved healthcare outcomes. This study introduces a novel ensemble framework that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, optimized using the Integer-Based Linear Hopper Optimization Algorithm (ILHOA). The model leverages CNN for spatial feature extraction and LSTM for capturing temporal patterns in clinical and laboratory data. ILHOA enhances model efficiency and robustness by selecting the most relevant features and eliminating redundancy. The proposed system includes data preprocessing, ILHOA-based feature selection, and a hybrid CNN-LSTM structure. Predictions from both deep learning models are combined through an ensemble process to boost generalization and reduce overfitting. The model’s performance is evaluated using key metrics including accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results demonstrate the superiority of the ILHOA-optimized CNN-LSTM model over traditional machine learning and standalone deep learning approaches, offering high classification accuracy and reduced computational complexity. The hybrid architecture also improves interpretability, making it suitable for real-time clinical decision-making. Future work will focus on integrating attention mechanisms and validating the model with real-world patient datasets to enhance generalizability and expand its applicability across broader healthcare diagnostics.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Parthiban M, Anna Alphy and Sreedevi B;
Methodology: Anna Alphy and Sreedevi B;
Writing- Original Draft Preparation: Anna Alphy and Sreedevi B;
Supervision: Anna Alphy and Sreedevi B;
Validation: Parthiban M and Anna Alphy;
Writing- Reviewing and Editing: Parthiban M, Anna Alphy and Sreedevi B;
All authors reviewed the results and approved the final version of the manuscript.
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Anna Alphy
Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi–NCR Campus, Ghaziabad, Uttar Pradesh, India.
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Cite this article
Parthiban M, Anna Alphy and Sreedevi B, “Integer Guided Linear Hopper Strategy in an LSTM Ensemble Framework for Prognosis Prediction”, Journal of Machine and Computing, vol.5, no.3, pp. 1373-1385, July 2025, doi: 10.53759/7669/jmc202505108.
