OPTIMIZATION OF PREDICTION OF LUNG DISORDERS USING LSTM COMPARISON OF RMSPROP AND ADAM
DOI:
https://doi.org/10.33480/jitk.v11i3.7767Keywords:
LSTM, Medical Time-Series, Pulmonary Disorder Prediction, Regression, RMSPropAbstract
Accurate prediction of pulmonary disorders is essential to support early diagnosis and clinical decision-making. Medical time-series data are inherently nonlinear and temporally dependent, making conventional statistical approaches insufficient. This study formulates pulmonary disorder prediction as a regression problem and proposes an optimized Long Short-Term Memory (LSTM) model by comparing two widely used optimization algorithms, RMSProp and Adam. The dataset consists of 30,000 clinical records obtained from an open-source Kaggle repository, including demographic, behavioral, and health-related variables relevant to respiratory conditions. Data preprocessing involved categorical encoding and Min–Max normalization, followed by an 80:20 train–test split. Model performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Experimental results demonstrate that the Adam optimizer achieves superior performance with lower prediction errors and more stable convergence compared to RMSProp and the baseline SGD optimizer. These findings highlight the critical role of optimizer selection in LSTM-based medical time-series modeling.
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[1] N. Shobeiri, A. Adel, and A. Rashki Kemmak, “Economic Burden of Diseases Caused by Air Pollution in Iran: A Cross-Sectional Study,” Heal. Sci. Reports, vol. 8, no. 9, 2025, doi: 10.1002/hsr2.71229.
[2] D. Teku, “Geo-environmental and socio-economic impacts of artisanal and small-scale mining in Ethiopia: challenges, opportunities, and sustainable solutions,” Front. Environ. Sci., vol. 13, no. March, pp. 1–16, 2025, doi: 10.3389/fenvs.2025.1505202.
[3] Y. Penders et al., “Burden of Respiratory Syncytial Virus Disease in Adults with Asthma and Chronic Obstructive Pulmonary Disease: A Systematic Literature Review,” Curr. Allergy Asthma Rep., vol. 25, no. 1, pp. 1–16, 2025, doi: 10.1007/s11882-025-01194-w.
[4] J. Y. Wu et al., “The clinical effectiveness of sodium-glucose co-transporter-2 inhibitors on prognosis of patients with chronic obstructive pulmonary disease and diabetes,” Nat. Commun. , vol. 16, no. 1, pp. 1–8, 2025, doi: 10.1038/s41467-025-60582-y.
[5] Y. Wang, R. Han, X. Ding, W. Feng, R. Gao, and A. Ma, “Chronic obstructive pulmonary disease across three decades: trends, inequalities, and projections from the Global Burden of Disease Study 2021,” Front. Med., vol. 12, no. March, pp. 1–15, 2025, doi: 10.3389/fmed.2025.1564878.
[6] M. A. Althobiani et al., “The role of digital health in respiratory diseases management: a narrative review of recent literature,” Front. Med., vol. 12, no. February, 2025, doi: 10.3389/fmed.2025.1361667.
[7] C. Nobili, M. Riccò, G. Piglia, and P. Manzoni, “Impact of Climate Change and Air Pollution on Bronchiolitis: A Narrative Review Bridging Environmental and Clinical Insights,” Pathogens, vol. 14, no. 7, pp. 1–13, 2025, doi: 10.3390/pathogens14070690.
[8] Y. Feng et al., “Exposure to living environmental risk factors and incidence of chronic lung disease in Chinese adults: results from a cross-sectional and longitudinal study,” BMC Public Health, vol. 25, no. 1, 2025, doi: 10.1186/s12889-025-24658-0.
[9] J. C. L. Chow, “Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review,” Algorithms, vol. 18, no. 3, pp. 1–19, 2025, doi: 10.3390/a18030156.
[10] et al., “Big Data and Machine Learning in Healthcare: A Business Intelligence Approach for Cost Optimization and Service Improvement,” Am. J. Med. Sci. Pharm. Res., vol. 07, no. 03, pp. 115–135, 2025, doi: 10.37547/tajmspr/volume07issue03-14.
[11] M. Pan et al., “Application of artificial intelligence in the health management of chronic disease: bibliometric analysis,” Front. Med., vol. 11, no. January, 2024, doi: 10.3389/fmed.2024.1506641.
[12] M. G. Hanna et al., “Future of Artificial Intelligence—Machine Learning Trends in Pathology and Medicine,” Mod. Pathol., vol. 38, no. 4, p. 100705, 2025, doi: 10.1016/j.modpat.2025.100705.
[13] M. M. R. Bhuiyan et al., “Transformation of Plant Breeding Using Data Analytics and Information Technology: Innovations, Applications, and Prospective Directions,” Front. Biosci. - Elit., vol. 17, no. 1, pp. 1–10, 2025, doi: 10.31083/FBE27936.
[14] F. Gyaase, O. S. Okunlola, A. M. Olusanya, A. Adedokun, O. I. Somoye, and A. T. Erhieyovwe, “Utilizing machine learning for predictive analysis of emission levels to ensure compliance in refinery operations,” Int. J. Eng. Technol., vol. 14, no. 1, pp. 24–29, 2025, doi: 10.14419/6x85gr51.
[15] A. Yunita et al., “Performance analysis of neural network architectures for time series forecasting: A comparative study of RNN, LSTM, GRU, and hybrid models,” MethodsX, vol. 15, 2025, doi: 10.1016/j.mex.2025.103462.
[16] L. A. Al-Samrraie et al., “Deep Learning Models Based on CNN, RNN, and LSTM for Rainfall Forecasting: Jordan as a Case Study,” Math. Model. Eng. Probl., vol. 12, no. 7, pp. 2456–2466, 2025, doi: 10.18280/mmep.120724.
[17] M. Nikesh, D. Rohini, M. Bharathi, and S. Hifsa Naaz, “Deep Dive into Document Classification: Fusion of RNN and LSTM Approach,” J. Knowl. Data Sci. Inf. Manag., vol. 2, no. 1, pp. 9–20, 2025, doi: 10.46610/jokdsim.2025.v02i01.002.
[18] T. Q. Minh et al., “Data reconstruction leverages one-dimensional Convolutional Neural Networks (1DCNN) combined with Long Short-Term Memory (LSTM) networks for Structural Health Monitoring (SHM),” Meas. J. Int. Meas. Confed., vol. 253, no. April, 2025, doi: 10.1016/j.measurement.2025.117810.
[19] L. Bukenya, O. S. Eyobu, and T. J. Oyana, “An Optimized LSTM Model for Diagnosis Prediction of Lower Respiratory Tract Infections Using a Minimalistic Data Set,” IEEE Access, vol. 13, no. January, pp. 60341–60354, 2025, doi: 10.1109/ACCESS.2025.3540359.
[20] X. Fan et al., “Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model - a study in Eastern China,” BMC Public Health, vol. 25, no. 1, 2025, doi: 10.1186/s12889-025-22430-y.
[21] Z. Wang, Y. Song, L. Pang, S. Li, and G. Sun, “Attention-Enhanced CNN-LSTM Model for Exercise Oxygen Consumption Prediction with Multi-Source Temporal Features,” Sensors, vol. 25, no. 13, 2025, doi: 10.3390/s25134062.
[22] H. Saleh, N. El-Rashidy, S. Mostafa, A. AlMohimeed, S. El-Sappagh, and Z. H. Ali, “Cloud based real-time multivariate multi-step prediction of systolic blood pressure and heart rate using temporal convolutional network and Apache Spark,” J. Big Data, vol. 12, no. 1, 2025, doi: 10.1186/s40537-025-01207-5.
[23] E. Ji, Y. Wang, S. Xing, and J. Jin, “Hierarchical Reinforcement Learning for Energy-Efficient API Traffic Optimization in Large-Scale Advertising Systems,” IEEE Access, vol. 13, no. July, pp. 142493–142516, 2025, doi: 10.1109/ACCESS.2025.3598712.
[24] S. Shao, Y. Tian, and Y. Zhang, “Deep reinforcement learning assisted surrogate model management for expensive constrained multi-objective optimization,” Swarm Evol. Comput., vol. 92, no. September 2024, p. 101817, 2025, doi: 10.1016/j.swevo.2024.101817.
[25] M. Uppal et al., “Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers,” Front. Bioeng. Biotechnol., vol. 11, no. September, pp. 1–15, 2023, doi: 10.3389/fbioe.2023.1257591.
[26] S. H. H. Madni, L. A/L Pathmanatan, M. Faheem, H. M. F. Shahzad, and S. Shah, “Exploring optimizer efficiency for facial expression recognition with convolutional neural networks,” J. Eng., vol. 2025, no. 1, pp. 1–29, 2025, doi: 10.1049/tje2.70060.
[27] I. Journal and O. F. Science, “A Comparative Study of Optimization,” pp. 803–810, 2025.
[28] X. Liu, H. Qi, S. Jia, Y. Guo, and Y. Liu, “Recent Advances in Optimization Methods for Machine Learning: A Systematic Review,” Mathematics, vol. 13, no. 13, pp. 1–29, 2025, doi: 10.3390/math13132210.
[29] A. M. Elshewey, A. H. Abed, D. S. Khafaga, A. A. Alhussan, M. M. Eid, and E. S. M. El-Kenawy, “Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory,” Sci. Rep., vol. 15, no. 1, pp. 1–21, 2025, doi: 10.1038/s41598-024-83592-0.
[30] G. Ayappan and S. Anila, “Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm,” Sci. Rep., vol. 15, no. 1, pp. 1–24, 2025, doi: 10.1038/s41598-025-85140-w.
[31] S. Srivastava, S. Sharma, P. Tanwar, G. Dubey, and M. Memoria, “Improving Health Care Analytics: LSTM Networks for Enhanced Risk Assessment,” Procedia Comput. Sci., vol. 259, pp. 11–22, 2025, doi: 10.1016/j.procs.2025.03.302.
[32] I. Wajid, D. Li, and Q. Wang, “Hybrid ensemble approaches for cardiovascular disease prediction: Leveraging interpretable AI for clinical insight,” Intell. Med., vol. 12, no. May, p. 100297, 2025, doi: 10.1016/j.ibmed.2025.100297.
[33] A. Ashwini, V. Chirchi, S. Balasubramaniam, and M. A. Shah, “Bio inspired optimization techniques for disease detection in deep learning systems,” Sci. Rep., vol. 15, no. 1, pp. 1–30, 2025, doi: 10.1038/s41598-025-02846-7.
[34] R. Kumar, C. T. Pan, Y. M. Lin, S. Yow-Ling, T. S. Chung, and U. G. S. Janesha, “Enhanced Multi-Model Deep Learning for Rapid and Precise Diagnosis of Pulmonary Diseases Using Chest X-Ray Imaging,” Diagnostics, vol. 15, no. 3, pp. 1–32, 2025, doi: 10.3390/diagnostics15030248.
[35] U. K. Lilhore et al., “Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification,” Sci. Rep., vol. 15, no. 1, pp. 1–27, 2025, doi: 10.1038/s41598-025-95311-4.
[36] A. H. Dakheel, M. R. Mohammed, Z. A. A. Alhuseen, and W. A. Hashim, “Deep Learning-Based Decision Support System for Predicting Pregnancy Risk Levels through Cardiotocograph (CTG) Imaging Analysis,” Intell. Autom. Soft Comput., vol. 40, no. 1, pp. 195–220, 2025, doi: 10.32604/iasc.2025.061622.
[37] S. Kumar et al., “Optimization of Neural Networks Using Mathematical Algorithms: An LSTM-Based Machine Learning Approach to Lung Cancer Diagnosis,” Adv. Nonlinear Var. Inequalities, vol. 28, no. 2, pp. 365–388, 2025, doi: 10.52783/anvi.v28.2052.
[38] N. A. Melyani et al., “Analysis Comparison Classification Image Disease Eye Using the CNN Algorithm, Inception V3, DenseNet 121 and MobileNet V2 Architecture Models,” Public Res. J. Eng. Data Technol. Comput. Sci., vol. 3, no. 1, pp. 42–58, 2025, doi: 10.57152/predatecs.v3i1.1559.
[39] A. I. Taloba and R. T. Matoog, “Detecting respiratory diseases using machine learning-based pattern recognition on spirometry data,” Alexandria Eng. J., vol. 113, no. November 2024, pp. 44–59, 2025, doi: 10.1016/j.aej.2024.11.009.
[40] S. Mei et al., “Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series,” npj Syst. Biol. Appl., vol. 11, no. 1, 2025, doi: 10.1038/s41540-025-00489-y.
[41] Y. Uzun, İ. Çetin, M. Kayr, and I. I. Spirometry, “Innovative Approach in Nursing Care : Artificial Intelligence-Assisted Incentive Spirometry Innovative Approach in Nursing Care : Arti fi cial,” pp. 0–20, 2025, doi: 10.20944/preprints202509.1571.v1.
[42] D. Park et al., “Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review,” Biomed. Eng. Lett., vol. 15, no. 4, pp. 717–734, 2025, doi: 10.1007/s13534-025-00486-4.
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