A novel explainable machine learning-based healthy ageing scale

Gašperlin Stepančič, Katarina (author), Ramovš, Ana (author), Ramovš, Jože (author), Košir, Andrej (author)

Background Ageing is one of the most important challenges in our society. Evaluating how one is ageing is important in many aspects, from giving personalized recommendations to providing insight for long-term care eligibility. Machine learning can be utilized for that purpose, however, user reservations towards “black-box” predictions call for increased transparency and explainability of results. This study aimed to explore the potential of developing a machine learning-based healthy ageing scale that provides explainable results that could be trusted and understood by informal carers.Methods In this study, we used data from 696 older adults collected via personal field interviews as part of independent research. Explanatory factor analysis was used to find candidate healthy ageing aspects. For visualization of key aspects, a web annotation application was developed. Key aspects were selected by gerontologists who later used web annotation applications to evaluate healthy ageing for each older adult on a Likert scale. Logistic Regression, Decision Tree Classifier, Random Forest, KNN, SVM and XGBoost were used for multi-classification machine learning. AUC OvO, AUC OvR, F1, Precision and Recall were used for evaluation. Finally, SHAP was applied to best model predictions to make them explainable. Results The experimental results show that human annotations of healthy ageing could be modelled using machine learning where among several algorithms XGBoost showed superior performance. The use of XGBoost resulted in 0.92 macro-averaged AuC OvO and 0.76 macro-averaged F1. SHAP was applied to generate local explanations for predictions and shows how each feature is influencing the prediction. Conclusion The resulting explainable predictions make a step toward practical scale implementation into decision support systems. The development of such a decision support system that would incorporate an explainable model could reduce user reluctance towards the utilization of AI in healthcare and provide explainable and trusted insights to informal carers or healthcare providers as a basis to shape tangible actions for improving ageing. Furthermore, the cooperation with gerontology specialists throughout the process also indicates expert knowledge as integrated into the model.

• Language: English
• Keywords: explainable artificial inteligence, healthy aging scale, XGBoost clasifier, machine learning
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: 19 str.
• Numbering: Vol. 24 , art. 317
• PID: 20.500.12556/RUL-168021
• UDC: 004.85
• ISSN on article: 1472-6947
• DOI: 10.1186/s12911-024-02714-w
• COBISS.SI-ID: 215209987
• Publication date in RUL: 25.03.2025

Record is a part of a journal

• Title: BMC medical informatics and decision making
• Shortened title: BMC Med Inform Decis Mak
• Publisher: BioMed Central
• ISSN: 1472-6947
• COBISS.SI-ID: 2440980

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: pojasnjevalna umetna inteligenca, skala zdravega staranja, razvrščevalnik XGBoost, strojno učenje

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0246
• Name: ICT4QoL - Informacijsko komunikacijske tehnologije za kakovostno življenje