Fast Decision Support for Infectious Diseases in Large Crowd Environments using NLP-Based Symptom-Driven Ensemble Learning Algorithms
International Journal of Medical Toxicology and Forensic Medicine,
Vol. 16 (2026),
1 Dey 2026
,
Page 1-7
https://doi.org/10.22037/ijmtfm.v16.52603
Abstract
Background: The rapid growth of online medical data has increased the complexity of infectious disease detection. Natural language processing (NLP) is playing a vital part in transforming disease discovery by effectively analyzing clinical text data. This study develops a novel Fast Decision Support for Disease Detection in Large Crowd Environments with Heuristic Optimization Algorithm (FDSDD-LCEHOA) using Natural Language Symptoms.
Methods: For feature extraction, the Word2Vec model has been used to convert text into meaningful vector representations that capture contextual relationships among symptoms. For classification, an ensemble of deep learning (DL) techniques, namely bidirectional long short-term memory (BiLSTM), temporal convolutional networks (TCNs), and convolutional autoencoder (CAE), is employed to enhance predictive performance. Moreover, the hyperparameters of the three ensemble models are tuned using the manta ray foraging optimization (MRFO) algorithm to achieve optimal parameter settings.
Results: The proposed FDSDD-LCEHOA model achieved an accuracy of 98.37%, demonstrating its effectiveness in classifying diseases from natural-language symptoms.
Conclusion: The experimental results show that the proposed model outperforms existing models, enabling real-time disease monitoring and early outbreak detection in large-crowd environments.
- Disease detection, Fast decision support, Deep learning, Natural language symptoms, Manta ray foraging optimization, Word2Vec
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References
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