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A novel explainable machine learning-based healthy ageing scale.

Katarina Gašperlin Stepančič1, Ana Ramovš2, Jože Ramovš2

  • 1IBM Slovenija d.o.o., Ameriška ulica 8, 1000, Ljubljana, Slovenia.

BMC Medical Informatics and Decision Making
|October 30, 2024
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Summary
This summary is machine-generated.

This study developed an explainable machine learning model to assess healthy ageing, offering trusted insights for informal carers and healthcare providers. The model uses XGBoost for superior performance and SHAP for transparent predictions, aiding in personalized care decisions.

Keywords:
Expert ratingsExplainabilityFactor analysisHealthy ageingMachine learningNovel scaleOlder adults

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Area of Science:

  • Gerontology and Artificial Intelligence
  • Computational Social Science

Background:

  • Ageing presents significant societal challenges, necessitating accurate methods for evaluating individual ageing processes.
  • Assessing healthy ageing is crucial for personalized recommendations and long-term care eligibility.
  • Machine learning (ML) offers potential for ageing assessment, but 'black-box' models face user reluctance due to lack of transparency.

Purpose of the Study:

  • To develop an explainable ML-based healthy ageing scale.
  • To provide transparent and understandable results for informal carers and healthcare providers.
  • To integrate expert knowledge into an AI-driven decision support system for ageing.

Main Methods:

  • Utilized data from 696 older adults via personal field interviews.
  • Employed explanatory factor analysis to identify key healthy ageing aspects.
  • Applied various ML algorithms (Logistic Regression, Decision Tree, Random Forest, KNN, SVM, XGBoost) and evaluated performance using AUC OvO, AUC OvR, F1, Precision, and Recall.
  • Integrated SHAP (SHapley Additive exPlanations) for model explainability.

Main Results:

  • Human annotations of healthy ageing were successfully modeled using ML.
  • XGBoost demonstrated superior performance, achieving 0.92 macro-averaged AUC OvO and 0.76 macro-averaged F1.
  • SHAP analysis provided local explanations, detailing feature influence on individual predictions.

Conclusions:

  • Explainable ML predictions represent a step towards practical implementation in decision support systems for ageing.
  • Integrating explainable AI can reduce user reluctance in healthcare, offering trusted insights for improved care.
  • The study successfully integrated gerontology expertise into the ML model development process.