Application of machine learning in measurement of ageing and geriatric diseases: A systematic review

Abstract

Background As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning (ML) has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and application of machine learning methods in this area. Methods This systematic review followed PRISMA guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. Peer-reviewed articles were searched in the PubMed database with a focus on ML methods and the older population. Results A total of 59 papers were selected from the 81 identified papers after going through title screening, abstract screening and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised ML methods. The neurodegenerative disorders were found to be the most researched disease, in which Alzheimer’s disease was focused the most. Among NCDs, diabetes mellitus, hypertension, cancer, kidney diseases, cardiovascular diseases were the included and other rare diseases like oral health related diseases and bone diseases were also explored in some papers. In terms of application of ML, risk prediction was most common approach. More than half of the studies have used supervised machine learning algorithm, among which logistic regression, random forest, XG Boost were frequently used methods. These ML methods were applied on variety of datasets including population-based data, hospital records and social media. Conclusion The review identified a wide range of studies that employed ML algorithms to analyse various diseases and datasets. While the application of ML in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations.

Unveiling Fall Triggers in Older Adults: A Machine Learning Graphical Model Analysis

“…While existing research primarily focuses on supervised ML, there is a gap in studies utilizing unsupervised ML to address a wide range of fall risk factors. Das and Dhillon (2023) revealed that half of the reviewed studies used supervised ML, among which logistic regression, random forest, and XG Boost are frequently used methods [ 20 ]. Moreover, the existing literature tends to evaluate a restricted number of variables.…”