The long-term effects of dual caregiving on the caregivers' well-being among middle-aged and older adults in Taiwan.

OBJECTIVES: As life expectancy is prolonged, older people may face increased burdens related to supporting multi-generational family members. This study is aimed toward examining the effects of such an emerging type of informal care on the well-being of caregivers. METHODS: Participants aged 50 and over from the Taiwan Longitudinal Study on Aging (1996-2007, n = 4,217) were analyzed. We categorized caregiving status according to different care recipients: 1) older adults only, 2) grandchildren only, 3) both older adults and grandchildren (dual caregiving), and 4) non-caregivers. Well-being was measured based on depressive symptoms and degree of life satisfaction. Generalized Estimation Equation models were used to examine the association between types of caregiving and the caregivers' state of well-being. RESULTS: After adjusting for all covariates, caregivers of older adults had significantly more depressive symptoms and less life satisfaction than non-caregivers, especially when caregiving for adults with ADL problems. In contrast, caregivers of grandchildren were not significantly affect either depression or life satisfaction as compared with non-caregivers. Interestingly, caregiving for both older adults and grandchildren had no significant effect on depression but positively affected the degree of life satisfaction. CONCLUSION: Our findings highlight that simultaneously taking care of both older adults and grandchildren can buffer negative feelings in caregivers or even improve their mental health.

The changes in feeling of loneliness after retirement among baby boomers and pre-boomers in Taiwan: Do work-family conflict before retirement and social engagement after retirement matter?

Significance: Few studies have analyzed how loneliness-related factors differ across generations for older adults in non-Western societies. Building upon the stress process model, this study aimed to explore the relationships between work-family conflict before retirement, social engagement after retirement and changes in loneliness after retirement among retirees across two birth cohorts (Baby Boomers and pre-Boomers) in Taiwan. Methods: Data from the Taiwan Health and Retirement Study, a nationwide retired cohort sample collected from two waves between 2015/2016 and 2018/2019, was analyzed. A total of 2370 retirees aged 50-74 years were included in the analysis after excluding those who died or were lost to follow-up. Multivariate multinomial logistic models were used to estimate four types of changes in loneliness: (1) remaining not lonely, (2) becoming not lonely, (3) becoming lonely, and (4) remaining lonely. Results: About two-thirds of the retirees remained not lonely, and less than 10% maintained their feelings of loneliness across two waves. Multinomial logit models showed that both cohorts who experienced work-family conflict before retirement and stressful life events after retirement had higher odds of remaining lonely than those who remained not lonely. However, an increase in social engagement, especially social contact, appeared to be a protective factor against becoming and remaining lonely for both cohorts. Yet, work-related characteristics before retirement were significantly related to the changes in loneliness among pre-Boomers rather than Baby Boomers. Conclusions: The results suggest that work-family conflict before retirement produces an exacerbating effect; in contrast, social engagement after retirement is beneficial to not feeling lonely across two birth cohorts in Taiwan. This investigation highlights the importance of social stressors occurring before retirement because these have an effect on retirees' feelings of loneliness beyond individual socioeconomic status.

A new approach to generating virtual samples to enhance classification accuracy with small data-a case of bladder cancer.

In the medical field, researchers are often unable to obtain the sufficient samples in a short period of time necessary to build a stable data-driven forecasting model used to classify a new disease. To address the problem of small data learning, many studies have demonstrated that generating virtual samples intended to augment the amount of training data is an effective approach, as it helps to improve forecasting models with small datasets. One of the most popular methods used in these studies is the mega-trend-diffusion (MTD) technique, which is widely used in various fields. The effectiveness of the MTD technique depends on the degree of data diffusion. However, data diffusion is seriously affected by extreme values. In addition, the MTD method only considers data fitted using a unimodal triangular membership function. However, in fact, data may come from multiple distributions in the real world. Therefore, considering the fact that data comes from multi-distributions, in this paper, a distance-based mega-trend-diffusion (DB-MTD) technique is proposed to appropriately estimate the degree of data diffusion with less impacts from extreme values. In the proposed method, it is assumed that the data is fitted by the triangular and trapezoidal membership functions to generate virtual samples. In addition, a possibility evaluation mechanism is proposed to measure the applicability of the virtual samples. In our experiment, two bladder cancer datasets are used to verify the effectiveness of the proposed DB-MTD method. The experimental results demonstrated that the proposed method outperforms other VSG techniques in classification and regression items for small bladder cancer datasets.

A Boundary-Information-Based Oversampling Approach to Improve Learning Performance for Imbalanced Datasets.

Oversampling is the most popular data preprocessing technique. It makes traditional classifiers available for learning from imbalanced data. Through an overall review of oversampling techniques (oversamplers), we find that some of them can be regarded as danger-information-based oversamplers (DIBOs) that create samples near danger areas to make it possible for these positive examples to be correctly classified, and others are safe-information-based oversamplers (SIBOs) that create samples near safe areas to increase the correct rate of predicted positive values. However, DIBOs cause misclassification of too many negative examples in the overlapped areas, and SIBOs cause incorrect classification of too many borderline positive examples. Based on their advantages and disadvantages, a boundary-information-based oversampler (BIBO) is proposed. First, a concept of boundary information that considers safe information and dangerous information at the same time is proposed that makes created samples near decision boundaries. The experimental results show that DIBOs and BIBO perform better than SIBOs on the basic metrics of recall and negative class precision; SIBOs and BIBO perform better than DIBOs on the basic metrics for specificity and positive class precision, and BIBO is better than both of DIBOs and SIBOs in terms of integrated metrics.

Social-Economic Environments and Depressive Symptoms in Community-Dwelling Adults: A Multi-Level Analysis for Two Nationwide Datasets in Taiwan.

Most studies have focused on factors associated with depression at the individual level, and evidence on ecological models linking social-economic features with depression is rare in Taiwan. This study aimed to use multi-level analysis to explore the effects of social-economic environments on depressive symptoms among Taiwanese adults. The 2009 National Health Interview Survey (NHIS) and the Age-Friendly Environments database were linked in this study. A total of 6602 adults aged 20 years and older were included in the analysis. A Chinese version of the 10-item CESD was used as the outcome measure. Three social indicators (population density, divorce rate, and crime rate) and three economic indicators (unemployment rate, per capita disposable income, and per capita government expenditures) at the ecological level were examined. Results showed that two social environments and two economic features were significantly associated with depressive symptoms. However, the effects of these factors were different by gender and age groups. The economic environments were critical for males and young adults aged 20-44 years old, whereas the social environments were significant for females and middle-aged and older adults. Intervention efforts for depression prevention should integrate ecological approaches into the effects of social-economic environments on depressive symptoms.

Detecting representative data and generating synthetic samples to improve learning accuracy with imbalanced data sets.

It is difficult for learning models to achieve high classification performances with imbalanced data sets, because with imbalanced data sets, when one of the classes is much larger than the others, most machine learning and data mining classifiers are overly influenced by the larger classes and ignore the smaller ones. As a result, the classification algorithms often have poor learning performances due to slow convergence in the smaller classes. To balance such data sets, this paper presents a strategy that involves reducing the sizes of the majority data and generating synthetic samples for the minority data. In the reducing operation, we use the box-and-whisker plot approach to exclude outliers and the Mega-Trend-Diffusion method to find representative data from the majority data. To generate the synthetic samples, we propose a counterintuitive hypothesis to find the distributed shape of the minority data, and then produce samples according to this distribution. Four real datasets were used to examine the performance of the proposed approach. We used paired t-tests to compare the Accuracy, G-mean, and F-measure scores of the proposed data pre-processing (PPDP) method merging in the D3C method (PPDP+D3C) with those of the one-sided selection (OSS), the well-known SMOTEBoost (SB) study, and the normal distribution-based oversampling (NDO) approach, and the proposed data pre-processing (PPDP) method. The results indicate that the classification performance of the proposed approach is better than that of above-mentioned methods.

A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets.

OBJECTIVE: Medical data sets are usually small and have very high dimensionality. Too many attributes will make the analysis less efficient and will not necessarily increase accuracy, while too few data will decrease the modeling stability. Consequently, the main objective of this study is to extract the optimal subset of features to increase analytical performance when the data set is small. METHODS: This paper proposes a fuzzy-based non-linear transformation method to extend classification related information from the original data attribute values for a small data set. Based on the new transformed data set, this study applies principal component analysis (PCA) to extract the optimal subset of features. Finally, we use the transformed data with these optimal features as the input data for a learning tool, a support vector machine (SVM). Six medical data sets: Pima Indians' diabetes, Wisconsin diagnostic breast cancer, Parkinson disease, echocardiogram, BUPA liver disorders dataset, and bladder cancer cases in Taiwan, are employed to illustrate the approach presented in this paper. RESULTS: This research uses the t-test to evaluate the classification accuracy for a single data set; and uses the Friedman test to show the proposed method is better than other methods over the multiple data sets. The experiment results indicate that the proposed method has better classification performance than either PCA or kernel principal component analysis (KPCA) when the data set is small, and suggest creating new purpose-related information to improve the analysis performance. CONCLUSION: This paper has shown that feature extraction is important as a function of feature selection for efficient data analysis. When the data set is small, using the fuzzy-based transformation method presented in this work to increase the information available produces better results than the PCA and KPCA approaches.

A learning method for the class imbalance problem with medical data sets.

In medical data sets, data are predominately composed of "normal" samples with only a small percentage of "abnormal" ones, leading to the so-called class imbalance problems. In class imbalance problems, inputting all the data into the classifier to build up the learning model will usually lead a learning bias to the majority class. To deal with this, this paper uses a strategy which over-samples the minority class and under-samples the majority one to balance the data sets. For the majority class, this paper builds up the Gaussian type fuzzy membership function and alpha-cut to reduce the data size; for the minority class, we use the mega-trend diffusion membership function to generate virtual samples for the class. Furthermore, after balancing the data size of classes, this paper extends the data attribute dimension into a higher dimension space using classification related information to enhance the classification accuracy. Two medical data sets, Pima Indians' diabetes and the BUPA liver disorders, are employed to illustrate the approach presented in this paper. The results indicate that the proposed method has better classification performance than SVM, C4.5 decision tree and two other studies.