scholarly journals Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea

2022 ◽  
Vol 12 ◽  
Author(s):  
Haewon Byeon
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Fatty Acid ◽  
Depressive Disorders ◽  
Subjective Health ◽  
Bayesian Regression ◽  
Living Alone ◽  
Study Objective ◽  
Naive Bayesian ◽  
Naïve Bayesian

This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South Koreans. To achieve the study objective, this study explored the main risk factors of depressive disorders using the stacking ensemble machine technique. Moreover, this study developed a nomogram that could help primary physicians easily interpret high-risk groups of depressive disorders in primary care settings based on the major predictors derived from machine learning. This study analyzed 582 female older adults (≥60 years old) living alone. The depressive disorder, a target variable, was measured using the Korean version of Patient Health Questionnaire-9. This study developed five single predictive models (GBM, Random Forest, Adaboost, SVM, XGBoost) and six stacking ensemble models (GBM + Bayesian regression, RandomForest + Bayesian regression, Adaboost + Bayesian regression, SVM + Bayesian regression, XGBoost + Bayesian regression, GBM + RandomForest + Adaboost + SVM + XGBoost + Bayesian regression) to predict depressive disorders. The naive Bayesian nomogram confirmed that stress perception, subjective health, n-6 fatty acid, n-3 fatty acid, mean hours of sitting per day, and mean daily sleep hours were six major variables related to the depressive disorders of female older adults living alone. Based on the results of this study, it is required to evaluate the multiple risk factors for depression including various measurable factors such as social support.

scholarly journals Risk factors for depression during the COVID-19 pandemic: a longitudinal study in middle-aged and older adults

BJPsych Open ◽  
2021 ◽  
Vol 7 (5) ◽  
Author(s):  
Jamie Rutland-Lawes ◽  
Anna-Stiina Wallinheimo ◽  
Simon L. Evans
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Longitudinal Study ◽  
Regression Models ◽  
Protective Factor ◽  
Significant Risk ◽  
Living Alone ◽  
Depression Symptoms ◽  
Middle Aged ◽  
Increased Risk

Background The COVID-19 pandemic and resultant social restrictions have had widespread psychological ramifications, including a rise in depression prevalence. However, longitudinal studies on sociodemographic risk factors are lacking. Aims To quantify longitudinal changes in depression symptoms during the pandemic compared with a pre-pandemic baseline, in middle-aged and older adults, and identify the risk factors contributing to this. Method A total of 5331 participants aged ≥50 years were drawn from the English Longitudinal Study of Ageing. Self-reported depression symptoms in June/July 2020 were compared with baseline data from 2–3 years prior. Regression models investigated sociodemographic and lifestyle variables that could explain variance in change in depression. Results Within-participant depression scores increased significantly from pre-pandemic levels: 14% met the criteria for clinical depression at baseline, compared with 26% during the pandemic. Younger age, female gender, higher depression scores at baseline, living alone and having a long-standing illness were significant risk factors. Gender-stratified regression models indicated that older age was protective for women only, whereas urban living increased risk among women only. Being an alcohol consumer was a protective factor among men only. Conclusions Depression in UK adults aged ≥50 years increased significantly during the pandemic. Being female, living alone and having a long-standing illness were prominent risk factors. Younger women living in urban areas were at particularly high risk, suggesting such individuals should be prioritised for support. Findings are also informative for future risk stratification and intervention strategies, particularly if social restrictions are reimposed as the COVID-19 crisis continues to unfold.

scholarly journals The Structural Equation Modeling of Personal Aspects, Environmental Aspects, and Happiness Among Older Adults Living Alone: A Cross-Sectional Study  

2021 ◽  
Author(s):  
Eun Jeong Hwang ◽  
In Ok Sim
Keyword(s):  
Older Adults ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Subjective Health ◽  
Living Alone ◽  
Path Coefficient ◽  
Social Activities ◽  
Subjective Health Status ◽  
Cross Sectional ◽  
Fit Index

Abstract Background The happiness of older adults living alone warrants attention because they are more vulnerable to unhappiness than those living with families. The present study aimed to construct and test a structural equation model to elucidate the causal relationship among participation in social activities, satisfaction with the neighborhood environment, subjective health status, and happiness in older adults living alone in South Korea. Methods Secondary data of 2,768 older adults (605 males and 2,163 females) living on their own were extracted from the 2017 Korean Community Health Survey and used in this cross-sectional study. Data were collected via self-reported questionnaires and analyzed using SPSS version 20.0 and AMOS version 20.0. Results The hypothetical model exhibited a good fit: χ2 = 342.06 (df = 58, p < .001), goodness-of-fit index = .98, adjected goodness-of-fit index = .97, root mean square error of approximation = .04, and nonstandard fit index = .92. Participation in social activities had a significant effect on participants’ subjective health status (path coefficient = .45, p = .001) and happiness (path coefficient = .20, p = .003). Conclusions Interventions to improve the health and happiness of older adults living alone should aim to enhance their social and physical environmental dimensions based on the participants' various social activities and their neighborhoods' characteristics.

scholarly journals Performance Analysis of Data Mining Techniques for the Prediction Breast Cancer Risk on Big Data

2021 ◽  
Vol 10 (1) ◽  
pp. 83
Author(s):  
Solmaz Sohrabei ◽  
Alireza Atashi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Risk Factors ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Early Detection ◽  
Breast Cancers ◽  
Data Mining Techniques ◽  
Naive Bayesian ◽  
Naïve Bayesian

Introduction: Early detection breast cancer Causes it most curable cancer in among other types of cancer, early detection and accurate examination for breast cancer ensures an extended survival rate of the patients. Risk factors are an important parameter in breast cancer has an important effect on breast cancer. Data mining techniques have a growing reputation in the medical field because of high predictive capability and useful classification. These methods can help practitioners to develop tools that allow detecting the early stages of breast cancer.Material and Methods: The database used in this paper is provided by Motamed Cancer Institute, ACECR Tehran, Iran. It contains of 7834 records of breast cancer patients clinical and risk factors data. There were 4008 patients (52.4%) with breast cancers (malignant) and the remaining 3617 patients (47.6%) without breast cancers (benign). Support vector machine, multi-layer perceptron, decision tree, K nearest neighbor, random forest, naïve Bayesian models were developed using 20 fields (risk factor) of the database because database feature was restrictions. Used 10-fold crossover for models evaluate. Ultimately, the comparison of the models was made based on sensitivity, specificity and accuracy indicators.Results: Naïve Bayesian and artificial neural network are better models for the prediction of breast cancer risks. Naïve Bayesian had accuracy of 93%, specificity of 93.32%, sensitivity of 95056%, ROC of 0.95 and artificial neural network had accuracy of 93.23%, specificity of 91.98%, sensitivity of 92.69%, and ROC of 0.8.Conclusion: Strangely the different artificial intelligent calculations utilized in this examination yielded close precision subsequently these techniques could be utilized as option prescient instruments in the bosom malignancy risk considers. The significant prognostic components affecting risk pace of bosom disease distinguished in this investigation, which were approved by risk, are helpful and could be converted into choice help devices in the clinical area.

scholarly journals Social isolation and loneliness as risk factors for hospital admissions for respiratory disease among older adults

Thorax ◽  
2020 ◽  
Vol 75 (7) ◽  
pp. 597-599 ◽  
Author(s):  
Feifei Bu ◽  
Keir Philip ◽  
Daisy Fancourt
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Respiratory Disease ◽  
Hospital Admissions ◽  
Social Contact ◽  
Living Alone ◽  
Social Risk ◽  
Social Risk Factors ◽  
Increased Risk

Rising hospital admissions due to respiratory disease (RD) are a major challenge to hospitals. This study explored modifiable social risk factors among 4478 older adults from the English Longitudinal Study of Ageing. Data were linked with administrative hospital records and mortality registry data (follow-up 9.6 years) and analysed using survival analysis accounting for competing risks. Living alone and social disengagement but not social contact or loneliness were associated with an increased risk of RD admissions, independent of socio-demographic, health and behaviour factors. Providing support for disengaged adults living alone who are at risk of RD admissions should be explored.

scholarly journals The structural equation modeling of personal aspects, environmental aspects, and happiness among older adults living alone: a cross-sectional study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Eun Jeong Hwang ◽  
In Ok Sim
Keyword(s):  
Older Adults ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Subjective Health ◽  
Living Alone ◽  
Path Coefficient ◽  
Social Activities ◽  
Subjective Health Status ◽  
Cross Sectional ◽  
Fit Index

Abstract Background The happiness of older adults living alone warrants attention because they are more vulnerable to unhappiness than those living with families. The present study aimed to construct and test a structural equation model to elucidate the relationship among participation in social activities, satisfaction with the neighborhood environment, subjective health status, and happiness in older adults living alone in South Korea. Methods Secondary data of 2768 older adults (605 males and 2163 females) living on their own were extracted from the 2017 Korean Community Health Survey and used in this cross-sectional study. Data were collected via self-reported questionnaires and analyzed using SPSS version 20.0 and AMOS version 20.0. Results The hypothetical model exhibited a good fit: χ2 = 342.06 (df = 58, p < .001), goodness-of-fit index = .98, adjected goodness-of-fit index = .97, root mean square error of approximation = .04, and nonstandard fit index = .92. Participation in social activities had a significant effect on participants’ subjective health status (path coefficient = .45, p = .001) and happiness (path coefficient = .20, p = .003). Conclusions Interventions to improve the health and happiness of older adults living alone should aim to enhance their social and physical environmental dimensions based on the participants’ various social activities and their neighborhoods’ characteristics.

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