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. 2023 Jan 27:56:e12475.
doi: 10.1590/1414-431X2023e12475. eCollection 2023.

Data-driven decision making for the screening of cognitive impairment in primary care: a machine learning approach using data from the ELSA-Brasil study

C Szlejf  1   2 , A F M Batista  3   4 , L Bertola  1 , P A Lotufo  1 , I M Benseãor  1 , A D P Chiavegatto Filho  3 , C K Suemoto  1   5
Affiliations

Data-driven decision making for the screening of cognitive impairment in primary care: a machine learning approach using data from the ELSA-Brasil study

C Szlejf et al. Braz J Med Biol Res. .

Abstract

The systematic assessment of cognitive performance of older people without cognitive complaints is controversial and unfeasible. Identifying individuals at higher risk of cognitive impairment could optimize resource allocation. We aimed to develop and test machine learning models to predict cognitive impairment using variables obtainable in primary care settings. In this cross-sectional study, we included 8,291 participants of the baseline assessment of the ELSA-Brasil study, who were aged between 50 and 74 years and were free of dementia. Cognitive performance was assessed with a neuropsychological battery and cognitive impairment was defined as global cognitive z-score below 2 standard deviations. Variables used as input to the prediction models included demographics, social determinants, clinical conditions, family history, lifestyle, and laboratory tests. We developed machine learning models using logistic regression, neural networks, and gradient boosted trees. Participants' mean age was 58.3±6.2 years, 55% were female. Cognitive impairment was present in 328 individuals (4%). Machine learning algorithms presented fair to good discrimination (areas under the ROC curve between 0.801 and 0.873). Extreme Gradient Boosting presented the highest discrimination, high specificity (97%), and negative predictive value (97%). Seventy-six percent of the individuals with cognitive impairment were included among the highest ranked individuals by this algorithm. In conclusion, we developed and tested a machine learning model to predict cognitive impairment based on primary care data that presented good discrimination and high specificity. These characteristics could support the detection of patients who would not benefit from cognitive assessment, facilitating the allocation of human and economic resources.

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Figures

Figure 1
Figure 1. Distribution of the receiver operating characteristic curves of the different machine learning algorithms on the test set. ANN: Artificial Neural Network; AUC: area under the receiver operating characteristic curve; LightGBM: Light Gradient Boosting Machine; LR: Logistic Regression; XGBOOST: Extreme Gradient Boosting Machine.
Figure 2
Figure 2. Importance of variables according to the Shapley values for the Extreme Gradient Boosting algorithm on the test set. The vertical axis lists the input variables and the horizontal axis measures their contribution in classifying cognitive deficit. The color red represents higher values and blue lower values. SHAP: Shapley Additive exPlanations.
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