Analysis of a Hybrid DNN–BiLSTM Framework for Longitudinal Prediction of Lung Disease Recurrence Using Clinical Data


Olha Musa(1*); Zainudin Sidik(2); Ifriandi Labolo(3); Muliati Badaruddin(4); Abdul Malik I. Buna(5);

(1) Universitas Ichsan Gorontalo Utara
(2) Universitas Negeri Gorontalo
(3) Universitas Ichsan Gorontalo Utara
(4) Universitas Ichsan Gorontalo Utara
(5) Universitas Ichsan Gorontalo Utara
(*) Corresponding Author

  

Abstract


Predicting lung disease recurrence from longitudinal clinical data remains challenging because irregular temporal patterns and heterogeneous patient characteristics reduce the effectiveness of conventional deep learning models. This study analyzes a hybrid Deep Neural Network (DNN)–Bidirectional Long Short-Term Memory (BiLSTM) framework for the longitudinal prediction of lung disease recurrence using clinical data collected between 2021 and 2024 from a referral hospital in Gorontalo. The dataset includes demographic information, laboratory examination results, clinical diagnoses, and longitudinal medical records. Lung disease recurrence is defined as the reappearance or worsening of the disease during longitudinal clinical follow-up after the initial diagnosis or treatment. The proposed framework combines DNN to learn complex nonlinear relationships among multivariate clinical features and BiLSTM to capture temporal dependencies across sequential patient observations. Model performance was evaluated using Accuracy, Precision, Recall, F1-score, and Root Mean Square Error (RMSE), and compared with standalone DNN and BiLSTM models. Experimental results demonstrate that the proposed hybrid framework consistently outperformed the individual models, achieving an improvement of approximately 7–10% across the evaluation metrics while providing more stable longitudinal prediction performance. Furthermore, multivariate analysis identified dominant clinical variables associated with lung disease recurrence, improving the interpretability of prediction results for clinical decision-making. These findings indicate that the proposed framework provides an effective computational approach for longitudinal clinical prediction and supports the development of intelligent clinical decision support systems for recurrence risk assessment


Keywords


Hybrid DNN–BiLSTM; Deep Learning Analysis; Longitudinal Clinical Data; Lung Disease Recurrence; Clinical Prediction

  
  

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doi  https://doi.org/10.33096/ilkom.v18i2.3222.404-419 		  

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References


WHO, Sustainable development goal indicator 3.9.1: mortality attributed to air pollution. 2024.

P. Sengodan, K. Srinivasan, R. Pichamuthu, and S. Matheswaran, “Early detection and classification of malignant lung nodules from CT images: An optimal ensemble learning,” Expert Syst. Appl., vol. 229, no. PA, p. 120361, 2023, doi: 10.1016/j.eswa.2023.120361.

M. Jasmine Pemeena Priyadarsini et al., “Lung Diseases Detection Using Various Deep Learning Algorithms,” J. Healthc. Eng., vol. 2023, 2023, doi: 10.1155/2023/3563696.

O. Musa, S. Syarif, and Z. Zainuddin, “Application of Deep Learning Models in Clinical Data Analysis for Lung Disease Diagnosis,” in 2025 8th International Conference on Trends in Electronics and Informatics (ICOEI), Apr. 2025, pp. 1214–1219, doi: 10.1109/ICOEI65986.2025.11013196.

S. H. Karaddi and L. D. Sharma, “Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks,” Expert Syst. Appl., vol. 211, no. August 2022, p. 118650, 2023, doi: 10.1016/j.eswa.2022.118650.

S. M. Shafi and S. K. Chinnappan, “Hybrid transformer-CNN and LSTM model for lung disease segmentation and classification,” PeerJ Comput. Sci., vol. 10, p. e2444, Dec. 2024, doi: 10.7717/peerj-cs.2444.

O. Musa, “Performance Analysis of a Hybrid Deep Learning Framework Integrating CNN , RNN , LSTM , and ResNet50 for Lung Disease Recurrence Prediction Using Chest X-Ray Images and Post-Recovery Clinical Data,” vol. 16, no. 2, pp. 33909–33915, 2026, doi: 10.48084/etasr.17083.

A. N. Patel et al., “An explainable transfer learning framework for multi-classification of lung diseases in chest X-rays,” Alexandria Eng. J., vol. 98, no. December 2023, pp. 328–343, 2024, doi: 10.1016/j.aej.2024.04.072.

E. Mahamud, N. Fahad, M. Assaduzzaman, S. M. Zain, K. O. M. Goh, and M. K. Morol, “An explainable artificial intelligence model for multiple lung diseases classification from chest X-ray images using fine-tuned transfer learning,” Decis. Anal. J., vol. 12, no. May, p. 100499, 2024, doi: 10.1016/j.dajour.2024.100499.

T. Wang and Y. Wang, “A deep learning approach integrating multi-dimensional features for expert matching in healthcare question answering communities,” no. October, pp. 1–15, 2025, doi: 10.3389/fpubh.2025.1633754.

B. Patnaik, D. Swapnesh, K. Nayak, and S. Sahoo, “Attention enhanced hybrid deep learning model with 1D-CNN and BiLSTM for automated sleep apnea detection,” pp. 1–37, 2025.

B. Abdikenov et al., “Future of Breast Cancer Diagnosis : A Review of DL and ML Applications and Emerging Trends for Multimodal Data,” IEEE Access, vol. 13, no. June, pp. 136101–136143, 2025, doi: 10.1109/ACCESS.2025.3585377.

M. G. Veerabaku, J. Nithiyanantham, S. Urooj, A. Q. Md, A. K. Sivaraman, and K. F. Tee, “Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection,” Biomedicines, vol. 11, no. 4, p. 1167, Apr. 2023, doi: 10.3390/biomedicines11041167.

M. Zhang, J. Bi, H. Yuan, Z. Wang, J. Zhang, and R. Buyya, “An enhanced hybrid deep neural network method for adjusted industrial time series prediction with variable operating states,” Expert Syst. Appl., vol. 299, p. 130029, Mar. 2026, doi: 10.1016/j.eswa.2025.130029.

D. Kesavulu and R. Kannadasan, “A hybrid CNN-XLSTM-GRU deep learning model with autoencoder-based feature selection for hypothyroidism diagnosis,” pp. 1–33, 2026, doi: 10.1371/journal.pone.0347203.

T. Peng, Y. Gu, Z. Ye, X. Cheng, and J. Wang, “A-LugSeg: Automatic and explainability-guided multi-site lung detection in chest X-ray images,” Expert Syst. Appl., vol. 198, no. March, p. 116873, 2022, doi: 10.1016/j.eswa.2022.116873.

F. A. Badish, A. Elbayoudi, and N. A. Badish, “Hybrid CNN-LSTM Network for Lung Cancer Detection from Chest X-ray Images,” Asian J. Math. Comput. Res., vol. 32, no. 3, pp. 133–146, Jul. 2025, doi: 10.56557/ajomcor/2025/v32i39465.

M. K. Islam, M. M. Rahman, M. S. Ali, S. M. Mahim, and M. S. Miah, “Enhancing lung abnormalities diagnosis using hybrid DCNN-ViT-GRU model with explainable AI: A deep learning approach,” Image Vis. Comput., vol. 142, no. January, 2024, doi: 10.1016/j.imavis.2024.104918.

Z. Yuan, Y. Jiang, J. Li, and H. Huang, “Hybrid-DNNs: Hybrid Deep Neural Networks for Mixed Inputs,” pp. 1–14, 2020.

L. Khan, A. Qazi, M. Alhajlah, and A. Mahmood, “Empowering Urdu sentiment analysis : an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT,” Complex Intell. Syst., vol. 11, no. 1, pp. 1–14, 2025, doi: 10.1007/s40747-024-01631-9.

Y. Karyadi, “Prediksi Kualitas Udara Dengan Metoda LSTM, Bidirectional LSTM, dan GRU,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 1, pp. 671–684, Mar. 2022, doi: 10.35957/jatisi.v9i1.1588.

Y. Dong, X. Li, M. He, and J. Li, “DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product Reviews DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product Reviews,” Appl. Artif. Intell., vol. 39, no. 1, 2025, doi: 10.1080/08839514.2025.2461809.

P. Dileep et al., “An automatic heart disease prediction using cluster-based bi-directional LSTM (C-BiLSTM) algorithm,” Neural Comput. Appl., vol. 35, no. 10, pp. 7253–7266, Apr. 2023, doi: 10.1007/s00521-022-07064-0.

S. Ramanjaneyulu et al., “Hybrid Machine Learning Approach for Accurate Lung Cancer Prediction Using Structured Data and Medical Imaging,” vol. 14, no. 13, pp. 313–321, 2025.

V. Çetin and O. Yıldız, “A comprehensive review on data preprocessing techniques in data analysis,” Pamukkale Univ. J. Eng. Sci., vol. 28, no. 2, pp. 299–312, 2022, doi: 10.5505/pajes.2021.62687.

K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Glob. Transitions Proc., vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.

S. Bharati, P. Podder, and M. R. H. Mondal, “Hybrid deep learning for detecting lung diseases from X-ray images,” Informatics Med. Unlocked, vol. 20, p. 100391, 2020, doi: 10.1016/j.imu.2020.100391.

S. Y. Sourab and M. A. Kabir, “A comparison of hybrid deep learning models for pneumonia diagnosis from chest radiograms,” Sensors Int., vol. 3, no. December 2021, p. 100167, 2022, doi: 10.1016/j.sintl.2022.100167.

M. K. Islam, M. M. Rahman, M. S. Ali, S. M. Mahim, and M. S. Miah, “Enhancing lung abnormalities diagnosis using hybrid DCNN-ViT-GRU model with explainable AI: A deep learning approach,” Image Vis. Comput., vol. 142, no. August 2023, p. 104918, 2024, doi: 10.1016/j.imavis.2024.104918.

Preeti Sharma, “Design And Implementation Of Deep Learning-Based Model For Lung Disease Detection And Classification,” Int. J. Appl. Math., vol. 38, no. 7s, pp. 1054–1062, Oct. 2025, doi: 10.12732/ijam.v38i7s.534.


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