To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG’s asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG’s asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients.

ObjectivesBreast cancer is the second most common cancer among Korean women. Because breast cancer is strongly associated with negative emotional and physical changes, early detection and treatment of breast cancer are very important. As a supporting tool for classifying breast cancer, we tried to identify the best meta-learner model in a stacking ensemble when the same machine learning models for the base learner and meta-learner are used.MethodsWe used machine learning models, such as the gradient boosted model, distributed random forest, generalized linear model, and deep neural network in a stacking ensemble. These models were used to construct a base learner, and each of them was used as a meta-learner again. Then, we compared the performance of machine learning models in the meta-learner to determine the best meta-learner model in the stacking ensemble.ResultsExperimental results showed that using the GBM as a meta-learner led to higher accuracy than that achieved with any other model for breast cancer data and using the GLM as a meta learner led to low root-mean-squared error for both sets of breast cancer data.ConclusionsWe compared the performance of every meta-learner model in a stacking ensemble as a supporting tool for classifying breast cancer. The study showed that using specific models as a metalearner resulted in better performance than single classifiers, and using GBM and GLM as a meta-learner is appropriate as a supporting tool for classifying breast cancer data.

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Depression is a high-risk mental illness that can lead to suicide. However, for a variety of reasons, such as a negative perception of mental illness, most patients with depressive symptoms are reluctant to go to the hospital and miss appropriate treatment. Therefore, a simple prescreening method that an individual can use to identify depression is needed. Most EEG measurement devices that individuals use have few channels. However, most studies using EEG to diagnose depression have been conducted in a professional multichannel EEG environment. Therefore, it is difficult for individuals to prescreen depression based on the results of the studies. In this study, we proposed a model that predicts depression by using EEG data measured by a few channels so that it can measure depression using the EEG data measured by an individual. In this study, brain waves measured in two channels were imaged using STFT transform and a spectrogram. The EEG image data was then used in a deep learning model. As a result of the performance evaluation, 75% accuracy was shown for the classification of image depression EEGs and normal image type EEGs. As a result, low channel EEG data for deep learning can be used as an auxiliary tool to proactively diagnose depressed patients.