Background Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy. Objective Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC) curve of the proposed model (0.749 ± 0.010) was larger than the Framingham risk score (FRS) (0.393 ± 0.010). Conclusions The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.
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Rare class imbalance problems, which involve the classification of minority or rare class, are difficult, because the size of the rare class is smaller than the majority class. Since majority class prediction is easy, its accuracy seems to be also high. However, the minority classes cannot be accurately predicted, and for this reason, when the prediction model performance is evaluated by considering only the accuracy, it does not indicate whether the model can predict the minority classes. Therefore, a rare class prediction technique is required. In this study, a rare class prediction model is proposed for minority class prediction. In addition, a dataset of a semiconductor manufacturing process with class imbalance problems was used to create a fault detection model. This prediction model uses data preprocessing to build the characteristics and data set required by the rare classes. To distinguish the rare classes related to the required characteristics, we used standard deviation and Euclidean distance to perform the feature selection. In addition, a particle swarm optimization-deep belief network was applied to create a classifier. The model proposed in this research presents outstanding performance and is appropriate for highly class imbalance problems. KEYWORDSclass imbalance problem, deep belief network, feature selection, particle swarm optimization, rare class classification INTRODUCTIONBecause of the issues with dig data and the development of deep learning techniques, the methods for building prediction models are in the spotlight. 1,2 Many AI-based prediction models, which use machine learning, data mining, databases, and statistical methods, are being proposed. Such prediction models based on state-of-the-art techniques are being applied in many fields, and there is a progressive increase in their industrial value. 3,4 For us to implement the prediction models accurately, it is necessary to analyze both domain knowledge and data. In addition, there is an increase in demand for obtaining useful knowledge from the collected data, and therefore, active research is being conducted on prediction models that are suitable for specific domains. 5,6 Thus, the importance of classification prediction techniques for class imbalance problems including class distribution, which is 1 of the main issues in the field of data mining, is increasing. 7-9 When the classes are balanced (balanced class), the ratios of the classes to be predicted are evenly distributed.Thus, by learning the data, a balanced predictive model that can predict all the classes can be generated. In the imbalance problem, the ratio of the category to be predicted is different. In this case, a classification prediction model that can predict only a specific class (rare class or majority class) is generated. For example, in the semiconductor manufacturing process, although most of the produced wafers are regular products, there is small probability for the production of irregular products. Therefore, a rare class prediction method is required to pre...
Transfusion support for hematopoietic stem cell transplantation (HSCT) is an essential part of supportive care, and compatible blood should be transfused into recipients. As leukocyte antigen (HLA) matching is considered first and as the blood group does not impede HSCT, major, minor, bidirectional, and RhD incompatibilities occur that might hinder transfusion and cause adverse events. Leukocyte reduction in blood products is frequently used, and irradiation should be performed for blood products, except for plasma. To mitigate incompatibility and adverse events, local transfusion guidelines, hospital transfusion committees, and patient management should be considered.
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