• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.420-422
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• 1998

All Fault Detection and Diagnosis(FDD) methods utilize classification techniques. The objective of this study was to demonstrate the application of classification techniques to the problem of diagnosing faults in data generated by a variable-air-volume(VAV) air-handling unit(AHU) simulation model and to describe the characteristics of the techniques considered. Artificial neural network classifier and fuzzy clustering classifier were considered for fault diagnostics.

• Perspectives in Nursing Science
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• v.5 no.1
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• pp.1-15
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• 2008

Background: Recent changes in the medical environment have increased the need for the home health care nursing in Korea. Even though the number of home health care patients is increasing, the major nursing problems have not been identified due to lack of a standardized nursing diagnosis. Aim: An investigative study was conducted to determine the frequency and appropriateness of nursing problems in hospital-based home health care patients in Korea using two internationally standardized nursing diagnosis classification systems. Methods: Nursing records of 249 hospital-based home health care patients were reviewed and nursing problems were identified using the North American Nursing Diagnosis Association Nursing Diagnosis Taxonomy I (NANDA) and the Clinical Care Classification of Nursing Diagnoses (CCC). Findings: Out of 463 nursing problems. 403 nursing problems were described using the NANDA whereas 427 nursing problems were described using the CCC. Nursing diagnoses not captured by the NANDA classification include nausea/vomiting, anorexia, risk for nutrition deficit, decreased blood pressure, dying process, blood sugar impairment. infection unspecified, and disuse syndrome. Nursing diagnoses not captured by the CCC include nausea/vomiting and anorexia. Conclusions: In describing nursing problems of home health care patients, it was found that the CCC was able to represent more diagnoses than the NANDA.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.69-77
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• 2018

The malignant melanoma accounts for about 1 to 3% of the total malignant tumor in the West, especially in the US, it is a disease that causes more than 9,000 deaths each year. Generally, skin lesions are difficult to detect the features through photography. In this paper, we propose a computer-aided diagnosis algorithm based on deep learning for classification of malignant melanoma and benign skin tumor in RGB channel skin images. The proposed deep learning model configures the tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to segment a skin lesion area in the dermoscopic image. We could implement algorithms to classify malignant melanoma and benign tumor using skin lesion image and results of expert's labeling in ResNet. The U-Net model obtained a dice similarity coefficient of 83.45% compared with results of expert's labeling. The classification accuracy of malignant melanoma obtained the 83.06%. As the result, it is expected that the proposed artificial intelligence algorithm will utilize as a computer-aided diagnosis algorithm and help to detect malignant melanoma at an early stage.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.35-44
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• 1998

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introduced Modular Artificial Neural Network(MANN) for this purpose. MANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trained by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing MANN with multitasking and message transfer between processes in SUN workstation. We tested MANN in reactor system.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1031-1040
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• 2004

Electrocardiogram being the recording of the heart's electrical activity provides valuable clinical information about heart's status. Many re-searches have been pursued for heart disease diagnosis using ECG so far. However, electrocardio-graph uses foreign diagnosis algorithm due to inaccuracy of diagnosis results for a heart disease. This paper suggests ECG data collection, data preprocessing and heart disease pattern classification using data mining. This classification technique is the FB(Frequent pattern Bayesian) classifier and is a combination of two data mining problems, naive bayesian and frequent pattern mining. FB uses Product Approximation construction that uses the discovered frequent patterns. Therefore, this method overcomes weakness of naive bayesian which makes the assumption of class conditional independence.

• Journal of Sasang Constitutional Medicine
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• v.19 no.1
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• pp.98-115
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• 2007

1. Objectives This study focuses on the Validity of the Questionnaire about Sasang Constitution Classification for Mongolians 2. Methods By using the way of backward elimination, certain variables are chosen from the 438 cases whose physical conditions are absolutely diagnosed. After that, discriminant analysis for the selected variables has been done to obtain the physical constitution equation and the accuracy ratio of diagnosis which are useful for physical constitution diagnosis. 3. Results and Conclusions (1) In tile Validity for the Questionnaire of Sasang Constitution Classification for Mongolians, the accuracy ratio of diagnosis of Taeyangin is 100%, Soyangin 62.5%, Taeumin 76.7%, and Soeumin 66.1% respectively as a result of the discriminant analysis employing Cronbach's alpha coefficient. On the whole, the accuracy ratio of diagnosis is 70.1%. (2). In the Validity for the Questionnaire of Sasang Constitution Classification for Mongolians, the accuracy ratio of diagnosis of 70.1% means that it beats the maximum chance criterion of 41.4% and the proportional chance criterion of 34.4% by 28.7% and 35.7% respectively. Conclusively, this questionnaire has discriminant power.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1058-1062
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• 2008

In clinical decision support system(CDSS), unlike rule-based expert method, appropriate data-driven machine learning method can easily provide the information of individual feature(clinical test) for disease classification. However, currently developed methods focus on the improvement of the classification accuracy for diagnosis. With the analysis of feature importance in classification, one may infer the novel clinical test sets which highly differentiate the specific diseases or disease states. In this background, we introduce a novel CDSS that integrate a classifier and feature selection module together. Random forest algorithm is applied for the classifier and the feature importance measure. The system selects the significant clinical tests discriminating the diseases by examining the classification error during backward elimination of the features. The superior performance of random forest algorithm in clinical classification was assessed against artificial neural network and decision tree algorithm by using breast cancer, diabetes and heart disease data in UCI Machine Learning Repository. The test with the same data sets shows that the proposed system can successfully select the significant clinical test set for each disease.

• Imaging Science in Dentistry
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• v.32 no.3
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• pp.129-134
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• 2002

Purpose : To implement the database system of oral and maxillofacial radiological images using a commercial medical image management software with personally developed classification code. Materials and methods : The image database was built using a slightly modified commercial medical image management software, Dr. Image v.2.1 (Bit Computer Co., Korea). The function of wild card '*' was added to the search function of this program. Diagnosis classification codes were written as the number at the first three digits, and radiographic technique classification codes as the alphabet right after the diagnosis code. 449 radiological films of 218 cases from January, 2000 to December, 2000, which had been specially stored for the demonstration and education at Dept. of OMF Radiology of Dankook University Dental Hospital, were scanned with each patient information. Results: Cases could be efficiently accessed and analyzed by using the classification code. Search and statistics results were easily obtained according to sex, age, disease diagnosis and radiographic technique. Conclusion : Efficient image management was possible with this image database system. Application of this system to other departments or personal image management can be made possible by utilizing the appropriate classification code system.

• Korean Journal of Radiology
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• v.21 no.10
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• pp.1115-1125
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• 2020

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a primary liver cancer (PLC) with both hepatocytic and cholangiocytic phenotypes. Recently, the World Health Organization (WHO) updated its histological classification system for cHCC-CCA. Compared to the previous WHO histological classification system, the new version no longer recognizes subtypes of cHCC-CCA with stem cell features. Furthermore, some of these cHCC-CCA subtypes with stem cell features have been recategorized as either hepatocellular carcinomas (HCCs) or intrahepatic cholangiocarcinomas (ICCs). Additionally, distinctive diagnostic terms for intermediate cell carcinomas and cholangiolocarcinomas (previous cholangiolocellular carcinoma subtype) are now recommended. It is important for radiologists to understand these changes because of its potential impact on the imaging-based diagnosis of HCC, particularly because cHCC-CCAs frequently manifest as HCC mimickers, ICC mimickers, or as indeterminate on imaging studies. Therefore, in this review, we introduce the 2019 WHO classification system for cHCC-CCA, illustrate important imaging features characteristic of its subtypes, discuss the impact on imaging-based diagnosis of HCC, and address other important considerations.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.1020-1028
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• 2011

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying artificial neural network. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.