Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review

Priya, R. L.; Jinny, S. Vinila

doi:10.24996/ijs.2021.62.9.29

Cited by 13 publications

(7 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another systematic literature review by Battineni et al suggested that in real-time clinical practice, there is no universally accepted approach for determining the optimal method, as each machine learning technique comes with its own set of strengths and limitations, however, Support Vector Machines (SVM) and Logistic Regression (LR) are two common machine learning methods that are used in most of the studies [ 94 ]. In another review article, the majority of the examined studies emphasized that the use of only machine learning methods or combining it with other intelligent techniques is popularly used to prevent emergencies [ 95 ]. This approach holds a significant promise for uncovering substantial patterns in both structured and unstructured datasets.…”

Section: Discussionmentioning

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.

show abstract

Section: Discussionmentioning

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

show abstract

“…The Arabic letters Meem and Seen obtained the highest classification accuracy, reaching 100%, for the ten Arabic fonts in the sizes 8-72, while for the small font sizes (14)(15)(16)(17)(18)(19)(20), the classification accuracy rate reached 90 percent. The accuracy for the letters Aeen, Taa, Haa, Laam, and Waaw is perfect and has reached 100% to classify in sizes from 14 to 72 for all fonts.…”

Section: Cmfmentioning

confidence: 95%

Optical Recognition System of Non-Dotted Arabic Letters

Kadhim,

Jawad,

Kadhum

et al. 2024

Iraqi Journal of Science

View full text Add to dashboard Cite

Writing with Arabic characters is a very sensitive operation because some characters are vertically nested and written in multiple ways. The infinite variety of font styles and sizes poses many challenges when designing Arabic recognition systems. In this article, we used deep learning to build an Arabic recognition system that could recognize dotted letters. Our dataset contains 3000 color images for ten letters ( م ,و ,ل ,ع , ,ص ,س,ح ,و ,ط ,ر د ). The training stage was achieved through 2250 images (10 classes) of this data, and 750 images were used for the validation stage. The classification accuracy of our proposed model is 98.67 percent. Experimental results show that Arabic font styles (Diwani, Hacent Dalal, Times New Roman, B Tanabe, and Kofi) have the best classification accuracy, so they are the preferred styles to use in Arabic writing texts. The font sizes 28, 36, 48, and 72 are the letters that have been classified with 100% rate accuracy using our trained model, so they are the font styles preferred to use in writing Arabic texts.

show abstract

“…The review mainly focused on shallow machine learning algorithms such as support vector machine, decision tree, Bayesian network, artificial neural network, etc. [8] conducted a review on elderly healthcare system based on shallow machine learning algorithms. The paper review the machine learning technique for detecting chronic ailment in elderly person.…”

Section: Introductionmentioning

confidence: 99%

Detecting Elderly Behaviors Based on Deep Learning for Healthcare: Recent Advances, Methods, Real-World Applications and Challenges

2022

View full text Add to dashboard Cite

Machine learning has been applied in healthcare domain for the development of smart devices to improve the life of the elderly persons in the society. Taking care of elderly person in the society is a critical issue that need automation. To proffer solution, many researchers developed deep learning algorithms smart devices for detecting elderly behavior to improve the elderly healthcare. Despite the progress made in the applications of deep learning algorithms in elderly healthcare systems, to the best of the author's knowledge no comprehensive recent development has been published on this interesting research area especially focusing on deep learning. In this paper, we presented a comprehensive recent development on the advances, methods and real world applications on developing smart devices for detecting elderly behavior for use in smart home, smart clinic, smart hospital and smart elderly nursing home for elderly person's healthcare. Theories of the deep learning algorithms, recent development recorded as regard to the applicability of deep learning in elderly healthcare systems and case studies were discussed. A taxonomy based on the data extracted from the applicability of deep learning algorithms in elderly healthcare systems is created to ease pointing out areas that need more attention. The article shows that the deep learning algorithm that received tremendous attention from researchers is convolutional neural network architecture and its variants. To help in future development of the research area, we highlighted the challenges associated to the applicability of deep learning algorithms in elderly healthcare system and pointed out new point of view for future research. The research community can use our review as a benchmark for proposing novel deep learning algorithms based smart devices to detect elderly behavior for elderly healthcare systems. Industries and organizations can use the paper as a guide in selecting machine learning based smart device for detecting elderly behavior for elderly healthcare support.INDEX TERMS Machine learning, deep learning algorithms, convolutional neural network, elderly person behaviour, VGG, Internet of Things, smart nursing home.

show abstract

Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review

Cited by 13 publications

References 29 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

Optical Recognition System of Non-Dotted Arabic Letters

Detecting Elderly Behaviors Based on Deep Learning for Healthcare: Recent Advances, Methods, Real-World Applications and Challenges

Contact Info

Product

Resources

About