Early identification of older individuals at risk of mobility decline with machine learning

Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach

BACKGROUND

Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Combining machine learning (ML) approaches is superior to traditional regression analysis for its ability to handle complex data regarding its characteristics of being high-dimensional, non-linear, and highly correlated. The study goal was to use ML algorithms to classify fall risks in community-dwelling older adults with the aid of an explainable artificial intelligence (XAI) approach to increase interpretability.

METHODS

A total of 215 participants were included for analysis. The input information included personal metrics and posturographic parameters obtained from a tracker-based posturography of four standing postures. Two classification criteria were used: with a previous history of falls and the timed-up-and-go (TUG) test. We used three meta-heuristic methods for feature selection to handle the large numbers of parameters and improve efficacy, and the SHapley Additive exPlanations (SHAP) method was used to display the weights of the selected features on the model.

RESULTS

The results showed that posturographic parameters could classify the participants with TUG scores higher or lower than 10 s but were less effective in classifying fall risk according to previous fall history. Feature selections improved the accuracy with the TUG as the classification label, and the Slime Mould Algorithm had the best performance (accuracy: 0.72 to 0.77, area under the curve: 0.80 to 0.90). In contrast, feature selection did not improve the model performance significantly with the previous fall history as a classification label. The SHAP values also helped to display the importance of different features in the model.

CONCLUSION

Posturographic parameters in standing can be used to classify fall risks with high accuracy based on the TUG scores in community-dwelling older adults. Using feature selection improves the model's performance. The results highlight the potential utility of ML algorithms and XAI to provide guidance for developing more robust and accurate fall classification models. Trial registration Not applicable.

Correlates of quality of life, happiness and life satisfaction among European adults older than 50 years: A machine-learning approach

BACKGROUND AND OBJECTIVES

Previous research has documented the role of different categories of psychosocial factors (i.e., sociodemographic factors, personality, subjective life circumstances, activity, physical health, and childhood circumstances) in predicting subjective well-being and quality of life among older adults. No previous study has simultaneously modeled a large number of these psychosocial factors using a well-powered sample and machine learning algorithms to predict quality of life, happiness, and life satisfaction among older adults. The aim of this paper was to investigate the correlates of quality of life, happiness, and life satisfaction among European adults older than 50 years using machine learning techniques.

RESEARCH DESIGN AND METHODS

Data drawn from the Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 7 were used. Participants were 62,500 persons aged 50 years and over living in 26 Continental EU Member States, Switzerland, and Israel. Multiple machine learning regression approaches were used.

RESULTS

The algorithms captured 53%, 33%, and 18% of the variance of quality of life, life satisfaction, and happiness, respectively. The most important categories of correlates of quality of life and life satisfaction were physical health and subjective life circumstances. Sociodemographic factors (mostly country of residence) and psychological variables were the most important categories of correlates of happiness.

DISCUSSION AND IMPLICATIONS

This study highlights subjective poverty, self-perceived health, country of residence, subjective survival probability, and personality factors (especially neuroticism) as important correlates of quality of life, happiness, and life satisfaction. These findings provide evidence-based recommendations for practice and/or policy implications.

Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study

BACKGROUND

Some studies have employed machine learning (ML) methods for mobility prediction modeling in older adults. ML methods could be a helpful tool for life-space mobility (LSM) data analysis.

AIM

This study aimed to evaluate the predictive value of ML algorithms for the restriction of life-space mobility (LSM) among elderly people and to identify the most important risk factors for that prediction model.

METHODS

A 2-year LSM reduction prediction model was developed using the ML-based algorithms decision tree, random forest, and eXtreme gradient boosting (XGBoost), and tested on an independent validation cohort. The data were collected from the International Mobility in Aging Study (IMIAS) from 2012 to 2014, comprising 372 older patients (≥ 65 years of age). LSM was measured by the Life-Space Assessment questionnaire (LSA) with five levels of living space during the month before assessment.

RESULTS

According to the XGBoost algorithm, the best model reached a mean absolute error (MAE) of 10.28 and root-mean-square error (RMSE) of 12.91 in the testing portion. The variables frailty (39.4%), mobility disability (25.4%), depression (21.9%), and female sex (13.3%) had the highest importance.

CONCLUSION

The model identified risk factors through ML algorithms that could be used to predict LSM restriction; these risk factors could be used by practitioners to identify older adults with an increased risk of LSM reduction in the future. The XGBoost model offers benefits as a complementary method of traditional statistical approaches to understand the complexity of mobility.