An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques

Salman Mahmood, Raza Hasan (), Saqib Hussain and Rochak Adhikari
Additional contact information
Salman Mahmood: Department of Science and Engineering, Nazeer Hussain University, ST-2, Near Karimabad, Karachi 75950, Pakistan
Raza Hasan: Department of Computer Science, Solent University, Southampton SO14 0YN, UK
Saqib Hussain: Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8QH, UK
Rochak Adhikari: Department of Computer Science, Solent University, Southampton SO14 0YN, UK

World, 2025, vol. 6, issue 1, 1-33

Abstract: Asthma remains a prevalent chronic condition, impacting millions globally and presenting significant clinical and economic challenges. This study develops a predictive model for asthma outcomes, leveraging automated machine learning (AutoML) and explainable AI (XAI) to balance high predictive accuracy with interpretability. Using a comprehensive dataset of demographic, clinical, and respiratory function data, we employed AutoGluon to automate model selection, optimization, and ensembling, resulting in a model with 98.99% accuracy and a 0.9996 ROC-AUC score. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) were applied to provide both global and local interpretability, ensuring that clinicians can trust and understand model predictions. Additionally, counterfactual analysis enabled hypothetical scenario exploration, supporting personalized asthma management by allowing clinicians to assess potential interventions for individual patient risk profiles. To facilitate clinical adoption, a Streamlit v1.41.0 application was developed for real-time access to predictions and interpretability. This study addresses key gaps in asthma prediction, notably in model transparency and generalizability, while providing a practical tool for enhancing personalized care. Future research could expand the validation across diverse patient populations to reinforce the model’s robustness in broader clinical environments.

Keywords: asthma prediction; machine learning; AutoML; explainable AI; SHAP; LIME; counterfactual analysis; healthcare predictive modeling; personalized medicine; Streamlit application (search for similar items in EconPapers)
JEL-codes: G15 G17 G18 L21 L22 L25 L26 Q42 Q43 Q47 Q48 R51 R52 R58 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2673-4060/6/1/15/pdf (application/pdf)
https://www.mdpi.com/2673-4060/6/1/15/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jworld:v:6:y:2025:i:1:p:15-:d:1566220

Access Statistics for this article

World is currently edited by Ms. Cassie Hu

More articles in World from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().