Analysis of the Correspondence of the Degree of Fragility with the Way to Exercise the Force of the Hand

Frailty is a geriatric syndrome characterized by increased individual vulnerability with an increase in both dependence and mortality when exposed to external stressors. The use of Frailty Indices in routine clinical practice is limited by several factors, such as the cognitive status of the patient...

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Bibliographic Details
Published in:	The Journal of frailty & aging Vol. 13; no. 3; pp. 248 - 253
Main Authors:	Guindal, E P, Parra, X, Musté, M, Pérez, C, Macho, O, Català, A
Format:	Journal Article
Language:	English
Published:	France Springer Nature B.V 2024
Subjects:	Age Aged Aged, 80 and over Artificial intelligence Clinical medicine Clinical trials Female Frail Elderly Frailty Frailty - diagnosis Geriatric Assessment - methods Geriatricians Geriatrics Grip strength Hand Strength - physiology Hands Health services Humans Hypotheses Learning strategies Machine Learning Male Muscle strength Muscle Strength Dynamometer Older people Patients Physical examinations Pilot Projects Questionnaires Sarcopenia Comprehensive Geriatric Assessment random forests frailty identification hand grip strength supervised learning
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Summary:	Frailty is a geriatric syndrome characterized by increased individual vulnerability with an increase in both dependence and mortality when exposed to external stressors. The use of Frailty Indices in routine clinical practice is limited by several factors, such as the cognitive status of the patient, times of consultation, or lack of prior information from the patient. In this study, we propose the generation of an objective measure of frailty, based on the signal from hand grip strength (HGS). This signal was recorded with a modified Deyard dynamometer and processed using machine learning strategies based on supervised learning methods to train classifiers. A database was generated from a cohort of 138 older adults in a transverse pilot study that combined classical geriatric questionnaires with physiological data. Participants were patients selected by geriatricians of medical services provided by collaborating entities. To process the generated information 20 selected significant features of the HGS dataset were filtered, cleaned, and extracted. A technique based on a combination of the Synthetic Minority Oversampling Technique (SMOTE) to generate new samples from the smallest group and ENN (technique based on K-nearest neighbors) to remove noisy samples provided the best results as a well-balanced distribution of data. A Random Forest Classifier was trained to predict the frailty label with 92.9% of accuracy, achieving sensitivities higher than 90%.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2260-1341 2273-4309
DOI:	10.14283/jfa.2024.46