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BACKGROUND: In practice, the collected datasets for data analysis are usually incomplete as some data contain missing attribute values. Many related works focus on constructing specific models to produce estimations to replace the missing values, to make the original incomplete datasets become complete. Another type of solution is to directly handle the incomplete datasets without missing value imputation, with decision trees being the major technique for this purpose. OBJECTIVE: To introduce a novel approach, namely Deep Learning-based Decision Tree Ensembles (DLDTE), which borrows the bounding box and sliding window strategies used in deep learning techniques to divide an incomplete dataset into a number of subsets and learning from each subset by a decision tree, resulting in decision tree ensembles. METHOD: Two medical domain problem datasets contain several hundred feature dimensions with the missing rates of 10% to 50% are used for performance comparison. RESULTS: The proposed DLDTE provides the highest rate of classification accuracy when compared with the baseline decision tree method, as well as two missing value imputation methods (mean and k-nearest neighbor), and the case deletion method. CONCLUSION: The results demonstrate the effectiveness of DLDTE for handling incomplete medical datasets with different missing rates. [ABSTRACT FROM AUTHOR]

Data discretization aims to transform a set of continuous features into discrete features, thus simplifying the representation of information and making it easier to understand, use, and explain. In practice, users can take advantage of the discretization process to improve knowledge discovery and data analysis on medical domain problem datasets containing continuous features. However, certain feature values were frequently missing. Many data-mining algorithms cannot handle incomplete datasets. In this study, we considered the use of both discretization and missing-value imputation to process incomplete medical datasets, examining how the order of discretization and missing-value imputation combined influenced performance. The experimental results were obtained using seven different medical domain problem datasets: two discretizers, including the minimum description length principle (MDLP) and ChiMerge; three imputation methods, including the mean/mode, classification and regression tree (CART), and k-nearest neighbor (KNN) methods; and two classifiers, including support vector machines (SVM) and the C4.5 decision tree. The results show that a better performance can be obtained by first performing discretization followed by imputation, rather than vice versa. Furthermore, the highest classification accuracy rate was achieved by combining ChiMerge and KNN with SVM. [ABSTRACT FROM AUTHOR]

Abstract Background The electrical signals measuring method is recommended to examine the relationship between neuronal activities and measure with the event related potentials (ERPs) during an auditory and a visual oddball paradigm between schizophrenic patients and normal subjects. The aim of this study is to discriminate the activation changes of different stimulations evoked by auditory and visual ERPs between schizophrenic patients and normal subjects. Methods Forty-three schizophrenic patients were selected as experimental group patients, and 40 healthy subjects with no medical history of any kind of psychiatric diseases, neurological diseases, or drug abuse, were recruited as a control group. Auditory and visual ERPs were studied with an oddball paradigm. All the data were analyzed by SPSS statistical software version 10.0. Results In the comparative study of auditory and visual ERPs between the schizophrenic and healthy patients, P300 amplitude at Fz, Cz, and Pz and N100, N200, and P200 latencies at Fz, Cz, and Pz were shown significantly different. The cognitive processing reflected by the auditory and the visual P300 latency to rare target stimuli was probably an indicator of the cognitive function in schizophrenic patients. Conclusions This study shows the methodology of application of auditory and visual oddball paradigm identifies task-relevant sources of activity and allows separation of regions that have different response properties. Our study indicates that there may be slowness of automatic cognitive processing and controlled cognitive processing of visual ERPs compared to auditory ERPs in schizophrenic patients. The activation changes of visual evoked potentials are more regionally specific than auditory evoked potentials.

Military personnel movement is exposed to solar radiation and sunburn is a major problem which can cause lost workdays and lead to disciplinary action. This study was designed to identify correlation parameters in evaluating in vivo doses and epidermis changes following sunburn inflammation. Several noninvasive bioengineering techniques have made objective evaluations possible. The volar forearms of healthy volunteers ( ), 2 areas, 20 mm in diameter, were irradiated with UVB 100 mj/ and 200 mj/ , respectively. The skin changes were recorded by several monitored techniques before and 24 hours after UV exposures. Our results showed that chromameter value provides more reliable information and can be adopted with mathematical model in predicting the minimal erythema dose (MED) which showed lower than visual assessment by 10 mj/ (Pearson correlation coefficient ). A more objective measure for evaluation of MED was established for photosensitive subjects' prediction and sunburn risks prevention.