Back to Search Start Over

Machine Learning-Based Routine Laboratory Tests Predict One-Year Cognitive and Functional Decline in a Population Aged 75+ Years

Authors :
Karina Braga Gomes
Ramon Gonçalves Pereira
Alexandre Alberto Braga
Henrique Cerqueira Guimarães
Elisa de Paula França Resende
Antônio Lúcio Teixeira
Maira Tonidandel Barbosa
Wagner Meira Junior
Maria das Graças Carvalho
Paulo Caramelli
Source :
Brain Sciences, Vol 13, Iss 4, p 690 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Background: Cognitive and functional decline are common problems in older adults, especially in those 75+ years old. Currently, there is no specific plasma biomarker able to predict this decline in healthy old-age people. Machine learning (ML) is a subarea of artificial intelligence (AI), which can be used to predict outcomes Aim: This study aimed to evaluate routine laboratory variables able to predict cognitive and functional impairment, using ML algorithms, in a cohort aged 75+ years, in a one-year follow-up study. Method: One hundred and thirty-two older adults aged 75+ years were selected through a community-health public program or from long-term-care institutions. Their functional and cognitive performances were evaluated at baseline and one year later using a functional activities questionnaire, Mini-Mental State Examination, and the Brief Cognitive Screening Battery. Routine laboratory tests were performed at baseline. ML algorithms—random forest, support vector machine (SVM), and XGBoost—were applied in order to describe the best model able to predict cognitive and functional decline using routine tests as features. Results: The random forest model showed better accuracy than other algorithms and included triglycerides, glucose, hematocrit, red cell distribution width (RDW), albumin, hemoglobin, globulin, high-density lipoprotein cholesterol (HDL-c), thyroid-stimulating hormone (TSH), creatinine, lymphocyte, erythrocyte, platelet/leucocyte (PLR), and neutrophil/leucocyte (NLR) ratios, and alanine transaminase (ALT), leukocyte, low-density lipoprotein cholesterol (LDL-c), cortisol, gamma-glutamyl transferase (GGT), and eosinophil as features to predict cognitive decline (accuracy = 0.79). For functional decline, the most important features were platelet, PLR and NLR, hemoglobin, globulin, cortisol, RDW, glucose, basophil, B12 vitamin, creatinine, GGT, ALT, aspartate transferase (AST), eosinophil, hematocrit, erythrocyte, triglycerides, HDL-c, and monocyte (accuracy = 0.92). Conclusions: Routine laboratory variables could be applied to predict cognitive and functional decline in oldest-old populations using ML algorithms.

Details

Language :
English
ISSN :
20763425
Volume :
13
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Brain Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.f26d6842b88e4dd59b7a92f458b42ca3
Document Type :
article
Full Text :
https://doi.org/10.3390/brainsci13040690