首页|Universidad Politecnica de Madrid Reports Findings in Machine Learning (FRELSA: A dataset for frailty in elderly people originated from ELSA and evaluated throu gh machine learning models)
Universidad Politecnica de Madrid Reports Findings in Machine Learning (FRELSA: A dataset for frailty in elderly people originated from ELSA and evaluated throu gh machine learning models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Madrid, Spain, by News Rx correspondents, research stated, “Frailty is an age-related syndrome characte rized by loss of strength and exhaustion and associated with multi-morbidity. Ea rly detection and prediction of the appearance of frailty could help older peopl e age better and prevent them from needing invasive and expensive treatments.” Our news journalists obtained a quote from the research from Universidad Politec nica de Madrid, “Machine learning techniques show promising results in creating a medical support tool for such a task. This study aims to create a dataset for machine learning-based frailty studies, using Fried’s Frailty Phenotype definiti on. Starting from a longitudinal study on aging in the UK population, we defined a frailty label for each subject. We evaluated the definition by training seven different models for detecting frailty with data that were contemporary to the ones used for the definition. We then integrated more data from two years before to obtain prediction models with a 24-month horizon. Features selection was per formed using the MultiSURF algorithm, which ranks all features in order of relev ance to the detection or prediction task. We present a new frailty dataset of 53 03 subjects and more than 6500 available features. It is publicly available, pro vided one has access to the original English Longitudinal Study of Ageing datase t. The dataset is balanced after grouping frailty with pre-frailty, and it is su itable for multiclass or binary classification and prediction problems. The seve n tested architectures performed similarly, forming a solid baseline that can be improved with future work. Linear regression achieved the best F-score and AURO C in detection and prediction tasks. Creating new frailty-annotated datasets of this size is necessary to develop and improve the frailty prediction techniques. We have shown that our dataset can be used to study and test machine learning m odels to detect and predict frailty.”
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