Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression

Baek, Jiwon; Chung, Kyungyong

doi:10.1109/access.2020.2968393

Cited by 69 publications

(38 citation statements)

References 40 publications

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“…Therefore, it is necessary to validate the result of the image classified on the basis of the decision boundary. For validation, precision, recall and F-measure are applied [33], [34]. Precision means a ratio of actual True data to the True data determined by the model.…”

Section: Decision Boundary Calculation Through Anomaly Score For Cmentioning

confidence: 99%

Decision Boundary-Based Anomaly Detection Model Using Improved AnoGAN From ECG Data

2020

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Arrhythmia detection through deep learning is mainly classified through supervised learning. Supervised learning progresses through the labeled data. However, in the medical field, it is challenging to collect ECG data of patients with arrhythmia than ECG data of healthy people, and thus data bias occurs. Therefore, if you use a supervised learning model, there are problems with lack of data and imbalance between labels that arise during learning. Accordingly, this study proposes the decision boundary-based Anomaly detection model using improved AnoGan from ECG data. In this study, at the time of learning, the loss of the Generator does not reduce, but the loss of a Discriminator lowers. Even if the Generator and Discriminator were designed to have the same learning count, the learning competency of Generator was judged to be lowered. In repeated experiments, it was found that the best loss balance was achieved when the learning count of Discriminator was 1 and that of Generator was 4. Another problem is that the decision boundary of AnoGAN is subjective. Accordingly, the repeated experiments based on F-measure are conducted to determine a decision boundary. For performance evaluation, the accuracy of the model is evaluated on the basis of Epoch, and the goodness-of-fit of the model is evaluated on the basis of AUC and F-measure. According to the evaluation of F-measure, the model has the best performance when the decision boundary is 200. In terms of Epoch, the model has the highest accuracy when the Epoch is 10. In addition, the proposed model has better goodness-of-fit than AnoGAN.

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Section: Decision Boundary Calculation Through Anomaly Score For Cmentioning

confidence: 99%

Decision Boundary-Based Anomaly Detection Model Using Improved AnoGAN From ECG Data

2020

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“…Therefore, in the big data-based machine learning, a deep neural network model combines multiple layers of the neural network and draws a meaningful outcome through efficient reasoning and learning. The learning structure of the deep neural network is suitable for massive learning data, and learning is made from the observed items, and thereby a proper approximate function can be created [23,24]. A deep neural network is favorable to the modeling of complicated nonlinear relations and is much used in the learning and recognition area.…”

Section: Machine Learning and Artificial Neural Network Modelsmentioning

confidence: 99%

A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

2020

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Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

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“…This means that there is usually one transaction for the user, so it is necessary to collect data from many users. Variables appearing in EMR such as health checkups and National Health and Nutrition Examination Survey [14] are used as non-time series data [20]. A large amount of data is analyzed with various data mining techniques and the results are used in universal services.…”

Section: B Deep Learning-based Smart Healthcare Modelmentioning

confidence: 99%

Hybrid Multi-Modal Deep Learning using Collaborative Concat Layer in Health Bigdata

Kim

Chung

2020

IEEE Access

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A health model based on data has various missing values depending on the user situation, and the accuracy of a health model requiring variables that the user cannot collect appears low. A deep learning health model is fitted by learning weights to increase accuracy. This implies that additional learning is required when the environment changes, such as when new variables are added or deleted depending on the user situation. In the process of applying a deep-learning-based health model to the user situation, accuracy may be degraded if learning is omitted. In this paper, we propose hybrid multimodal deep learning using a collaborative concat layer in health big data. The proposed method uses a machine learning technique to alleviate the issue caused by the change in the data observation range according to a change in the user situation, and occurring in multimodal health deep learning. It is a layer composed of the connection, input, and output of the model of the collaborative node (CN). A CN is a node that predicts absent variables through filtering using the similarity of input values. With CN, a collaborative concat layer (CCL) that handles missing values from the input of the health model can be configured, and the issue related to missing values occurring in the health model can be resolved. With the proposed CCL, it is possible to reuse existing models or construct new models through the concatenation of several single-modal deep learning models. By evaluating the effect on the input and output of the model according to the structural position of the CCL, various networks can be configured, and the performance of the single-modal model can be maintained. In particular, the accuracy of a deep learning model is more stable when the CCL is used, suggesting the experiment progress based on the assumption that a specific variable is absent depending on the user situation.

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Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression

Cited by 69 publications

References 40 publications

Decision Boundary-Based Anomaly Detection Model Using Improved AnoGAN From ECG Data

Decision Boundary-Based Anomaly Detection Model Using Improved AnoGAN From ECG Data

A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Hybrid Multi-Modal Deep Learning using Collaborative Concat Layer in Health Bigdata

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