In practice, many medical domain datasets are incomplete, containing a proportion of incomplete data with missing attribute values. Missing value imputation can be performed to solve the problem of incomplete datasets. To impute missing values, some of the observed data (i.e., complete data) are generally used as the reference or training set, and then the relevant statistical and machine learning techniques are employed to produce estimations to replace the missing values. Since the collected dataset usually contains a certain number of feature dimensions, it is useful to perform feature selection for better pattern recognition. Therefore, the aim of this paper is to examine the effect of performing feature selection on missing value imputation of medical datasets. Experiments are carried out on five different medical domain datasets containing various feature dimensions. In addition, three different types of feature selection methods and imputation techniques are employed for comparison. The results show that combining feature selection and imputation is a better choice for many medical datasets. However, the feature selection algorithm should be carefully chosen in order to produce the best result. Particularly, the genetic algorithm and information gain models are suitable for lower dimensional datasets, whereas the decision tree model is a better choice for higher dimensional datasets.
This article proposes a fast mode decision algorithm based on the correlation of the just-noticeable-difference (JND) and the rate distortion cost (RD cost) to reduce the computational complexity of H.264/AVC. First, the relationship between the average RD cost and the number of JND pixels is established by Gaussian distributions. Thus, the RD cost of the Inter 16 × 16 mode is compared with the predicted thresholds from these models for fast mode selection. In addition, we use the image content, the residual data, and JND visual model for horizontal/vertical detection, and then utilize the result to predict the partition in a macroblock. From the experimental results, a greater time saving can be achieved while the proposed algorithm also maintains performance and quality effectively.
The safety of high-alert medication treatment is still a challenge all over the world. Approximately one-half of adverse drug events (ADEs) are related to high-alert medications, which motivates us to improve the predicament faced in clinical practice. The purpose of this study is to use machine-learning techniques to predict the risk of high-alert medication treatment. Taking the cardiovascular drug digoxin as an example, we collected the records of 513 patients who received the pertinent therapy during hospitalization at a tertiary medical center in Taiwan. Considering serum digoxin concentration (SDC) is the primary indicator for assessing the risk of digoxin therapy, patients with SDC being controlled at the recommended range before their discharge were defined as a low-risk population; otherwise, patients were defined as the high-risk population. Weka 3.9.4—an open source machine learning software—was adopted to develop binary classification models to predict the risk of digoxin therapy by a number of machine-learning techniques, including k-nearest neighbors (kNN), decision tree (C4.5), support vector machine (SVM), random forest (RF), artificial neural network (ANN) and logistic regression (LGR). The results showed that the performance of RF was the best, followed by C4.5 and ANN; the remaining classifiers performed poorly. This study confirmed that machine-learning techniques can yield favorable prediction effectiveness for high-alert medication treatment, thereby decreasing the risk of ADEs and improving medication safety.
A strategy called N-class replacement (NR) policy is proposed to Overcome visitor location register (VLR) overtlow in a mobile communiation system. The NR policy requires only few bits in the VLR record It is feasible for separated VLR databases in real mobile networks. For the rate of excellent replacement, the simulation results show that the NR policy outperforms currently proposed policies.
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