Machine learning approaches for frailty detection, prediction and classification in elderly people: A systematic review

• Published in: International journal of medical informatics (Shannon, Ireland) Ireland), 2023-10, Vol.178, p.105172-105172, Article 105172
• Main Authors: Leghissa, Matteo, Carrera, Álvaro, Iglesias, Carlos A.
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Cognitive frailty; Elderly; Frailty; Frailty index; Fried frailty phenotype; Machine learning; Systematic review
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/j.ijmedinf.2023.105172

Frailty in older people is a syndrome related to aging that is becoming increasingly common and problematic as the average age of the world population increases. Detecting frailty in its early stages or, even better, predicting its appearance can greatly benefit health in later years of life and save the healthcare system from high costs. Machine Learning models fit the need to develop a tool for supporting medical decision-making in detecting or predicting frailty. In this review, we followed the PRISMA methodology to conduct a systematic search of the most relevant Machine Learning models that have been developed so far in the context of frailty. We selected 41 publications and compared them according to their purpose, the type of dataset used, the target variables, and the results they obtained, highlighting their shortcomings and strengths. The variety of frailty definitions allows many problems to fall into this field, and it is often challenging to compare results due to the differences in target variables. The data types can be divided into gait data, usually collected with sensors, and medical records, often in the context of aging studies. The most common algorithms are well-known models available from every Machine Learning library. Only one study developed a new framework for frailty classification, and only two considered Explainability. This review highlights some gaps in the field of Machine Learning applied to the assessment and prediction of frailty, such as the need for a universal quantitative definition. It emphasizes the need for close collaboration between medical professionals and data scientists to unlock the potential of data collected in hospital and clinical settings. As a suggestion for future work, the area of Explainability, which is crucial for models in medicine and health care, was considered in very few studies. •The wide variety of frailty definitions and types of data used in the models makes it challenging to compare results.•Machine learning models fit the need for a detection and prediction tool for frailty in older populations.•Many well-known algorithms have been applied to the task, but very few frailty-specific ones have been developed.•In future work, Explainability needs to be considered for the medical community to accept data-based assessments.