Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm

Lee, Jin-Seong; Cho, Seo‐Eun; Geem, Zong Woo; Kim, Yong Ku

doi:10.1016/j.neulet.2020.134804

Cited by 36 publications

(22 citation statements)

References 23 publications

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“…Finally, the results of five times were summarized, and the average value was used as the estimation of model performance index. The classification of our model adopts the following indexes: accuracy, precision, recall, and F1 score are used as the final index (44). The relationship between the actual class and predicted class is shown in Table 1, and the evaluate metrics in this study are defined as Equations (11)(12)(13)(14).…”

Section: Experiments Resultsmentioning

confidence: 99%

Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network

Liu

Yuan

Tang

2022

Front. Public Health

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Alzheimer's disease (AD) is a neurodegenerative disease involving the decline of cognitive ability with illness progresses. At present, the diagnosis of AD mainly depends on the interviews between patients and doctors, which is slow, expensive, and subjective, so it is not a better solution to recognize AD using the currently available neuropsychological examinations and clinical diagnostic criteria. A recent study has indicated the potential of language analysis for AD diagnosis. In this study, we proposed a novel feature purification network that can improve the representation learning of transformer model further. Though transformer has made great progress in generating discriminative features because of its long-distance reasoning ability, there is still room for improvement. There exist many common features that are not indicative of any specific class, and we rule out the influence of common features from traditional features extracted by transformer encoder and can get more discriminative features for classification. We apply this method to improve transformer's performance on three public dementia datasets and get improved classification results markedly. Specifically, the method on Pitt datasets gets state-of-the-art (SOTA) result.

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Section: Experiments Resultsmentioning

confidence: 99%

Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network

Liu

Yuan

Tang

2022

Front. Public Health

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“…The classification results of the decision tree model adopt the following indexes: accuracy, precision, recall, specificity, and F1 score [ 3 ]. The above-indicators are calculated by a confusion matrix.…”

Section: Resultsmentioning

confidence: 99%

“…Depression is a common mental health disease [ 1 , 2 ]. Depressive patients are usually accompanied by depressed mood, loss of interest, mental retardation, voice movement reduction, self-criticism negation, insomnia, lack of appetite, suicidal thoughts, and other symptoms, and their life becomes boring and meaningless [ 3 , 4 ]. Depression has the characteristics of a slow onset, long treatment cycle, high incidence rate, and being difficult to diagnose.…”

Section: Introductionmentioning

confidence: 99%

A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health

Chen

Pan

2021

IJERPH

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Depression is a common mental health disease, which has great harm to public health. At present, the diagnosis of depression mainly depends on the interviews between doctors and patients, which is subjective, slow and expensive. Voice data are a kind of data that are easy to obtain and have the advantage of low cost. It has been proved that it can be used in the diagnosis of depression. The voice data used for modeling in this study adopted the authoritative public data set, which had passed the ethical review. The features of voice data were extracted by Python programming, and the voice features were stored in the format of CSV files. Through data processing, a big database, containing 1479 voice feature samples, was generated for modeling. Then, the decision tree screening model of depression was established by 10-fold cross validation and algorithm selection. The experiment achieved 83.4% prediction accuracy on voice data set. According to the prediction results of the model, the patients can be given early warning and intervention in time, so as to realize the health management of personal depression.

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“…However, we forbear from comparisons with most of these studies, owing to the fundamental differences between such researches and our study, for example, in terms of sample type (mostly patients or certain area elderly vs. national community), sample size, study design (mostly cross-sectional vs. prospectively assessed data), data source (electronic health record data vs. national epidemiological data), and age group (almost exclusively adults vs. 60 years old and above). The only exception in terms of comparability is the North Korea study by Na et al ( 42 ) who also used a representative national community sample to prospectively predict depression, and got an AUC value of 0.870 (random forest). However, the data they used consisted of a larger sample of 6,588 respondents (the study population is for all adults) with a CESD-11 and the 9 points of positive screening value.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Regression and Machine Learning Methods in Depression Forecasting Among Home-Based Elderly Chinese: A Community Based Study

Lin

Fang

2022

Front. Psychiatry

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BackgroundDepression is highly prevalent and considered as the most common psychiatric disorder in home-based elderly, while study on forecasting depression risk in the elderly is still limited. In an endeavor to improve accuracy of depression forecasting, machine learning (ML) approaches have been recommended, in addition to the application of more traditional regression approaches.MethodsA prospective study was employed in home-based elderly Chinese, using baseline (2011) and follow-up (2013) data of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort study. We compared four algorithms, including the regression-based models (logistic regression, lasso, ridge) and ML method (random forest). Model performance was assessed using repeated nested 10-fold cross-validation. As the main measure of predictive performance, we used the area under the receiver operating characteristic curve (AUC).ResultsThe mean AUCs of the four predictive models, logistic regression, lasso, ridge, and random forest, were 0.795, 0.794, 0.794, and 0.769, respectively. The main determinants were life satisfaction, self-reported memory, cognitive ability, ADL (activities of daily living) impairment, CESD-10 score. Life satisfaction increased the odds ratio of a future depression by 128.6% (logistic), 13.8% (lasso), and 13.2% (ridge), and cognitive ability was the most important predictor in random forest.ConclusionsThe three regression-based models and one ML algorithm performed equally well in differentiating between a future depression case and a non-depression case in home-based elderly. When choosing a model, different considerations, however, such as easy operating, might in some instances lead to one model being prioritized over another.

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Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm

Cited by 36 publications

References 23 publications

Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network

Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network

A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health

Comparison of Regression and Machine Learning Methods in Depression Forecasting Among Home-Based Elderly Chinese: A Community Based Study

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