Article: Predicting chronic obstructive pulmonary disease using machine learning with bio-inspired hyperparameter optimisation Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.4 pp.227 - 242 Abstract: Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory condition for which early detection is crucial to effective patient management. With LGBM and DTC as the foundational models, this study explores the predictive capability of ML approaches for COPD. Two bio-inspired optimisers, the TSA and ROA, were employed to enhance their performance. These optimisers mimic the collective behaviour of biological systems, such as tunicates' foraging patterns and jellyfish's pulsating movements, to achieve optimal solutions within the model training process. Relevant features are extracted from patient data, potentially including demographics, medical history, lung function tests, and lifestyle factors. Among the metrics used to evaluate the performance of the optimised models are their accuracy and precision. The DTTS model's excellent performance shows how well the DTC model predicts COPD. The greatest accuracy and precision scores of 0.907 and 0.911 support its COPD prediction accuracy. These findings demonstrate the DTTS model's reliability and potential for early COPD identification and management. The DTTS model's strong accuracy and precision metrics suggest it could improve respiratory patient care and clinical decision-making. Inderscience Publishers - linking academia, business and industry through research

Title: Predicting chronic obstructive pulmonary disease using machine learning with bio-inspired hyperparameter optimisation

Authors: Yalin Song

Addresses: Department of Pharmacy, Zibo Vocational Institute, Zibo – 255300, Shandong, China

Abstract: Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory condition for which early detection is crucial to effective patient management. With LGBM and DTC as the foundational models, this study explores the predictive capability of ML approaches for COPD. Two bio-inspired optimisers, the TSA and ROA, were employed to enhance their performance. These optimisers mimic the collective behaviour of biological systems, such as tunicates' foraging patterns and jellyfish's pulsating movements, to achieve optimal solutions within the model training process. Relevant features are extracted from patient data, potentially including demographics, medical history, lung function tests, and lifestyle factors. Among the metrics used to evaluate the performance of the optimised models are their accuracy and precision. The DTTS model's excellent performance shows how well the DTC model predicts COPD. The greatest accuracy and precision scores of 0.907 and 0.911 support its COPD prediction accuracy. These findings demonstrate the DTTS model's reliability and potential for early COPD identification and management. The DTTS model's strong accuracy and precision metrics suggest it could improve respiratory patient care and clinical decision-making.

Keywords: chronic obstructive pulmonary; decision tree classification; DTC; light gradient boosting classification; LGBC; light gradient boosting machine; LGBM; rhizostoma optimisation algorithm; ROA; tunicate swarm algorithm; TSA; machine learning; ML.

DOI: 10.1504/IJRIS.2026.155205

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.4, pp.227 - 242

Received: 03 Oct 2024
Accepted: 08 Feb 2025
Published online: 29 Jul 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Predicting chronic obstructive pulmonary disease using machine learning with bio-inspired hyperparameter optimisation

Keep up-to-date