Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset

Moradifar, Pooyan; Amini, Hossein; Amiri, Mohammad

doi:10.1186/s12902-022-01222-0

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…Studies have developed predictive models (39,40) based on supervised ML (logistic regression, XG Boost, decision tree etc.) and some studies identi ed associated risk factors (41,42) using clustering algorithms (like principal component analysis), logistic regression classi er and other supervised ML algorithms. Predicative models for hypertension were developed by using population based datasets (43), with association of risk factors like high waist circumference (44), cognitive impairment (45) and sleep & pulmonary measures(46) were discussed.…”

Section: Resultsmentioning

confidence: 99%

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

Das

Dhillon

2023

Preprint

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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.

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Section: Resultsmentioning

confidence: 99%

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

Das

Dhillon

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Studies have developed predictive models [ 34 , 35 ] based on supervised ML (logistic regression, XG Boost, decision tree, etc.) and some studies identified associated risk factors [ 32 , 33 ] using clustering algorithms (like principal component analysis), logistic regression classifiers, and other supervised ML algorithms. Predicative models for hypertension were developed by using population-based datasets [ 39 ], with the association of risk factors like high waist circumference [ 38 ], cognitive impairment [ 37 ], and sleep & pulmonary measures [ 36 ] discussed.…”

Section: Resultsmentioning

confidence: 99%

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

Das,

Dhillon

2023

BMC Geriatr

View full text Add to dashboard Cite

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 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 the application of machine learning methods in this area. Methods This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (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. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used. Results A total of 70 papers were selected from the 120 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 machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer’s disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data. Conclusion The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning 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. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.

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Hyperglycemia screening based on survey data: an international instrument based on WHO STEPs dataset

Cited by 2 publications

References 31 publications

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

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

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

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