Clinical decision support system for quality of life among the elderly: an approach using artificial neural network

Ahmadi, Maryam; Nopour, Raoof

doi:10.1186/s12911-022-02044-9

Cited by 8 publications

(4 citation statements)

References 40 publications

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“…Finally, the five-choice Likert scales were transformed into two states of “pleasant” and “unpleasant” and registered in the database based on the gerontologists' opinions. 44 …”

Section: Methodsmentioning

confidence: 99%

Developing a prediction model for successful aging among the elderly using machine learning algorithms

2023

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Objective The aging phenomenon has an increasing trend worldwide which caused the emergence of the successful aging (SA)1 concept. It is believed that the SA prediction model can increase the quality of life (QoL)2 in the elderly by decreasing physical and mental problems and enhancing their social participation. Most previous studies noted that physical and mental disorders affected the QoL in the elderly but didn't pay much attention to the social factors in this respect. Our study aimed to build a prediction model for SA based on the physical, mental, and specially more social factors affecting SA. Methods The 975 cases related to SA and non-SA of the elderly were investigated in this study. We used the univariate analysis to determine the best factors affecting the SA. AB3, XG-Boost J-48, RF4, artificial neural network5, support vector machine6, and NB7 algorithms were used for building the prediction models. To get the best model predicting the SA, we compared them using positive predictive value (PPV)8, negative predictive value (NPV)9, sensitivity, specificity, accuracy, F-measure, and area under the receiver operator characteristics curve (AUC). Results Comparing the machine learning10 model's performance showed that the random forest (RF) model with PPV = 90.96%, NPV = 99.21%, sensitivity = 97.48%, specificity = 97.14%, accuracy = 97.05%, F-score = 97.31%, AUC = 0.975 is the best model for predicting the SA. Conclusions Using prediction models can increase the QoL in the elderly and consequently reduce the economic cost for people and societies. The RF can be considered an optimal model for predicting SA in the elderly.

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“…Finally, the five-choice Likert scales were transformed into two states of “pleasant” and “unpleasant” and registered in the database based on the gerontologists' opinions. 44 …”

Section: Methodsmentioning

confidence: 99%

Developing a prediction model for successful aging among the elderly using machine learning algorithms

2023

Self Cite

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“…Each layer has at least one neuron, which connects to neurons in different layers. This structure is characterized by simplicity and clarity, allowing each neuron to establish an appropriate linear or non-linear relationship between input and output [ 19 , 20 ]. According to the training mode of BP-ANN in current study, the input layer and hidden layer have 10 nodes respectively.…”

Section: Methodsmentioning

confidence: 99%

Global incidence, prevalence and disease burden of silicosis: 30 years’ overview and forecasted trends

Liu

Jiang

et al. 2023

BMC Public Health

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Background Globally, silicosis accounts for 90% of all pneumoconiosis cases and is a serious public health issue. It is characterized by progressive inflammation and irreversible pulmonary fibrosis. A comprehensive analysis at temporal, spatial and population levels with the most updated data from GBD 2019 is provided in this study to estimate the disease burden of silicosis from 1990 to 2019 and make predictions to 2029. Methods We delineated silicosis data on incidence, prevalence, and disability-adjusted life years (DALYs) as well as age-standardized rates (ASRs) across 30 years from GBD 2019. Joinpoint regression analysis was employed to detect temporal changes and estimate annual percentage change (APC) of each trend segment. Measures were stratified by time, location, age, and sociodemographic index (SDI). Back propagation artificial neural network (BP-ANN) model was applied to elaborate ASR trends from 1990 to 2019 and projections to the next 10 years. Results Globally, silicosis incident, prevalent cases, and DALYs increased by 64.6%, 91.4%, and 20.8%, respectively. However, all the corresponding ASRs showed overall downward trends with an estimated average APC (AAPC) of -0.5(-0.7 to -0.3), -0.2(-0.5 to 0.0), and − 2.0(-2.2 to -1.8), respectively. Middle and high-middle SDI regions carried the heaviest disease burden. The highest disease burden of silicosis was mainly transferred to the older from 1990 to 2019. The trend of ASRs demonstrated a rapid decline between 2005 and 2019, followed by a continuous decline until 2029. Conclusion Though disease burden of silicosis has been on a decline in general from 1990 to 2019, which shows a promising prospect but cannot be ignored. We should pay more attention to implementing preventive tactics and improving the life quality of present sufferers.

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“…Quality of life questionnaires have been used in combination with neural networks that factor in all parameters simultaneously [36]. The Aging Phenotype Calculator presented in this study enables assessing the current functional status of older adults based on the results of their examination and geriatric assessment.…”

Section: Aging Phenotype Calculator and Survival Predictionmentioning

confidence: 99%

Machine Learning-Based Decision-Making in Geriatrics: Aging Phenotype Calculator and Survival Prognosis

2024

Aging dis

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Aging is a natural process with varying effects. As we grow older, our bodies become more susceptible to aging-associated diseases. These diseases, individually or collectively, lead to the formation of distinct aging phenotypes. Identifying these aging phenotypes and understanding the complex interplay between coexistent diseases would facilitate more personalized patient management, a better prognosis, and a prolonged lifespan. Many studies distinguish between successful aging and frailty. However, this simple distinction fails to reflect the diversity of underlying causes. In this study, we sought to establish the underlying causes of frailty and determine the patterns in which these causes converge to form aging phenotypes. We conducted a comprehensive geriatric examination, cognitive assessment, and survival analysis of 2,688 long-living adults (median age = 92 years). The obtained data were clustered and used as input data for the Aging Phenotype Calculator, a multiclass classification model validated on an independent dataset of 96 older adults. The accuracy of the model was assessed using the receiver operating characteristic curve and the area under the curve. Additionally, we analyzed socioeconomic factors that could contribute to specific aging patterns. We identified five aging phenotypes: non-frailty, multimorbid frailty, metabolic frailty, cognitive frailty, and functional frailty. For each phenotype, we determined the underlying diseases and conditions and assessed the survival rate. Additionally, we provided management recommendations for each of the five phenotypes based on their distinct features and associated challenges. The identified aging phenotypes may facilitate better-informed decision-making. The Aging Phenotype Calculator (ROC AUC = 92%) may greatly assist geriatricians in patient management.

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Clinical decision support system for quality of life among the elderly: an approach using artificial neural network

Cited by 8 publications

References 40 publications

Developing a prediction model for successful aging among the elderly using machine learning algorithms

Developing a prediction model for successful aging among the elderly using machine learning algorithms

Global incidence, prevalence and disease burden of silicosis: 30 years’ overview and forecasted trends

Machine Learning-Based Decision-Making in Geriatrics: Aging Phenotype Calculator and Survival Prognosis

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