The presence of ketonemia was significantly lower than the presence of ketonuria. Weight loss per week was the only independent factor found to be associated with increased levels of 3HB. The clinical significance of this small increase requires further investigation.

The aim of the present study is to comparatively assess the performance of different machine learning and statistical techniques with regard to their ability to estimate the risk of developing type 2 diabetes mellitus (Case 1) and cardiovascular disease complications (Case 2). This is the first work investigating the application of ensembles of artificial neural networks (EANN) towards producing the 5‐year risk of developing type 2 diabetes mellitus and cardiovascular disease as a long‐term diabetes complication. The performance of the proposed models has been comparatively assessed with the performance obtained by applying logistic regression, Bayesian‐based approaches, and decision trees. The models' discrimination and calibration have been evaluated using the classification accuracy (ACC), the area under the curve (AUC) criterion, and the Hosmer–Lemeshow goodness of fit test. The obtained results demonstrate the superiority of the proposed models (EANN) over the other models. In Case 1, EANN with different topologies has achieved high discrimination and good calibration performance (ACC = 80.20%, AUC = 0.849, p value = .886). In Case 2, EANN based on bagging has resulted in good discrimination and calibration performance (ACC = 92.86%, AUC = 0.739, p value = .755).

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Abstract. Although Fourier and Wavelet Transform have been widely used for texture classification methods in medical images, the discrimination performance of FDCT has not been investigated so far in respect to breast cancer detection. Ιn this paper, three multi-resolution transforms, namely the Discrete Wavelet Transform (DWT), the Stationary Wavelet Transform (SWT) and the Fast Discrete Curvelet Transform (FDCT) were comparatively assessed with respect to their ability to discriminate between malignant and benign breast tumors in Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI). The mean and entropy of the detail sub-images for each decomposition scheme were used as texture features, which were subsequently fed as input into several classifiers. FDCT features fed to a Linear Discriminant Analysis (LDA) classifier produced the highest overall classification performance (93,18 % Accuracy).

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Dietary assessment can be crucial for the overall well-being of humans and at least in some instances for the prevention and management of chronic, life-threatening diseases. Recall and manual record keeping methods for food intake monitoring are available, but often inaccurate when applied for a long period of time. On the other hand, automatic record keeping approaches that adopt mobile cameras and computer vision methods seem to simplify the process and can improve current human-centric diet monitoring methods. Here we present an extended critical literature overview of image-based food recognition systems (IBFRS) combining a camera of the user's mobile device with computer vision methods and publicly available food datasets (PAFD). In brief, such systems consist of several phases, such as the segmentation of the food items on the plate, the classification of the food items in a specific food category, and the estimation phase of volume, calories or nutrients of each food item. 159 studies were screened in this systematic review of IBFRS. A detailed overview of the methods adopted in each of the 78 included studies of this systematic review of IBFRS is provided along with their performance on PAFD. Studies that included IBFRS without presenting their performance in at least one of the abovementioned phases were excluded. Among the included studies, 45 (58%) studies adopted deep learning methods and especially Convolutional Neural Networks (CNNs) in at least one phase of the IBFRS with input PAFD. Among the implemented techniques, CNNs outperform all other approaches on the PAFD with a large volume of data, since the richness of these datasets provides adequate training resources for such algorithms. We also present evidence for the benefits of application of IBFRS in professional dietetic practice. Furthermore, challenges related to the IBFRS presented here are also thoroughly discussed along with future directions.