• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.523-544
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• 2018

Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.97-98
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• 2014

Vibration signal has been widely utilized in the diagnostics of rotating mechanical system. Diagnostics systems in rotating machinery are depends on the vibration data which are acquired from the system. However, the characteristics of acquired data can be vary according to the position of anomaly installed or the position of data acquired. In this research, the coordinate transform of journal bearing vibration signal was proposed to overcome the limitation mentioned above. Journal bearing systems are equipped two gap sensors with ninety degree angles, and it can enable to generate coordinate transformed signals in arbitrary angle domain. More abundant information for each normal or anomaly conditions are obtained from coordinate transformation than only the data of the existing measuring position, then we have developed a reliable and robust diagnosis algorithm for journal bearing system.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.463-471
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• 2019

The variable transformations are useful ways to guarantee the functional relationships in the model. However, the presence of outliers may undermine the accuracy of transformation. This paper deals with response transformations in the partial linear models under the existence of outliers. A new procedure for response transformation and outliers detection is proposed. The procedure uses a sequential method for identifying outliers and dynamic graphical methods for an appropriate transformation. The graphical tools make it possible to catch diagnostic information by monitoring the movement of points in the data. The procedure is illustrated with several examples. Examples show that visual clues regarding the optimal transformation, the fittness of the model and the outlyness of the observations can be checked from the series of plots.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.557-560
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• 2023

In this paper, we introduce a pre-training method leveraging the capabilities of the Vision Transformer (ViT) for disease diagnosis in conventional Fundus images. Recognizing the need for effective representation learning in medical images, our method combines the Vision Transformer with a Masked Autoencoder to generate meaningful and pertinent image augmentations. During pre-training, the Masked Autoencoder produces an altered version of the original image, which serves as a positive pair. The Vision Transformer then employs contrastive learning techniques with this image pair to refine its weight parameters. Our experiments demonstrate that this dual-model approach harnesses the strengths of both the ViT and the Masked Autoencoder, resulting in robust and clinically relevant feature embeddings. Preliminary results suggest significant improvements in diagnostic accuracy, underscoring the potential of our methodology in enhancing automated disease diagnosis in fundus imaging.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• Parasites, Hosts and Diseases
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• v.61 no.2
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• pp.202-209
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• 2023

Lophomonas blattarum is an anaerobic protozoan living in the intestine of cockroaches and house dust mites, with ultramicroscopic characteristics such as the presence of a parabasal body, axial filament, and absence of mitochondria. More than 200 cases of Lophomonas infection of the respiratory tract have been reported worldwide. However, the current diagnosis of such infection depends only on light microscopic morphological findings from respiratory secretions. In this study, we attempted to provide more robust evidence of protozoal infection in an immunocompromised patient with atypical pneumonia, positive for Lophomonas-like protozoal cell forms. A direct search of bronchoalveolar lavage fluid via polymerase chain reaction (PCR), transmission electron microscopy (TEM), and metagenomic next-generation sequencing did not prove the presence of protozoal infection. PCR results were not validated with sufficient rigor, while de novo assembly and taxonomic classification results did not confirm the presence of an unidentified pathogen. The TEM results implied that such protozoal forms in light microscopy are actually non-detached ciliated epithelial cells. After ruling out infectious causes, the patient's final diagnosis was drug-induced pneumonitis. These findings underscore the lack of validation in the previously utilized diagnostic methods, and more evidence in the presence of L. blattarum is required to further prove its pathogenicity.

• Journal of Microbiology and Biotechnology
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• v.33 no.8
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• pp.1091-1100
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• 2023

Human papillomavirus (HPV) types 16 and 18 are the major causes of cervical lesions and are associated with 71% of cervical cancer cases globally. However, public health infrastructures to support cervical cancer screening may be unavailable to women in low-resource areas. Therefore, sensitive, convenient, and cost-efficient diagnostic methods are required for the detection of HPV16/18. Here, we designed two novel methods, real-time ERA and ERA-LFD, based on enzymatic recombinase amplification (ERA) for quick point-of-care identification of the HPV E6/E7 genes. The entire detection process could be completed within 25 min at a constant low temperature (35-43℃), and the results of the combined methods could be present as the amplification curves or the bands presented on dipsticks and directly interpreted with the naked eye. The ERA assays evaluated using standard plasmids carrying the E6/E7 genes and clinical samples exhibited excellent specificity, as no cross-reaction with other common HPV types was observed. The detection limits of our ERA assays were 10⁰ and 10¹ copies/µl for HPV16 and 18 respectively, which were comparable to those of the real-time PCR assay. Assessment of the clinical performance of the ERA assays using 114 cervical tissue samples demonstrated that they are highly consistent with real-time PCR, the gold standard for HPV detection. This study demonstrated that ERA-based assays possess excellent sensitivity, specificity, and repeatability for HPV16 and HPV18 detection with great potential to become robust diagnostic tools in local hospitals and field studies.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.801-809
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• 2024

In the digital age, the importance of personal information has magnified, underscoring the need for enhanced personal information protection, especially within public institutions. Despite ongoing efforts since 2007, significant breaches in public sector information underline persistent vulnerabilities. This study advocates for a transition from a diagnostic to an assessment framework to fortify privacy management in public institutions, as mandated by recent legislative revisions. The amended Personal Information Protection Act introduces an assessment approach, aiming to comprehensively assess and mitigate risks by expanding the scope of evaluation and implementing robust regulatory measures. This study examines the limitations of the current diagnostic practices through literature review and case analysis and proposes a systematic approach to adopting the new assesment system. By enhancing the assessment framework, the study expects to improve the effectiveness of personal information management in public institutions, thereby restoring public trust and ensuring a stable progression into a more secure digital era. The transition to an assessment system is designed not only to address the gaps in the current framework but also to provide a methodical assessment that supports ongoing improvement and compliance with enhanced legal standards.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.5
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• pp.2605-2610
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• 2016

The International Myeloma Working Group considers the serum free light chain (SFLC) assay to be an adjunct to traditional tests. Apart from the FLC ratio, the absolute values of individual free light chains also are gaining importance as they appear to be more relevant in certain clinical settings. Automated assays are available for their determination. As laboratories put new test systems into use catering to different disease populations, they are required by accreditation and certification bodies to verify or establish performance specifications, including reference intervals (RIs) representative of their population. Our aim was to establish local RIs for SFLC in a multicentre representative healthy population using a robust method. There was no significant relationship between SFLC levels and age, gender and creatinine levels. The 95% RI for ${\kappa}SFLC$ was 4.81 to 33.86mg/L, for ${\lambda}$ SFLC was 5.19 to 23.67mg/L and for ${\kappa}/{\lambda}SFLC$ was 0.36 to 2.33, significantly higher than the values given by the manufacturer. The ${\kappa}/{\lambda}$ SFLC ratio at 2.23, covering 100% of the data, showed 72% sensitivity (95% CI=39.0 - 94.0), 100% specificity (95% CI=71.5 - 100.0), 100% PPV (95% CI=21.5 - 100.0), 95% NPV (95% CI=75.4 - 99.9), and 79% accuracy (95% CI=56.0 - 93.0). In the patient group, kit RI for ${\kappa}/{\lambda}$ SFLC ratio classified 45.5% (n=5) as positive vs 9.1% (n=1) positive by the study RI, while the kit RI for kappa FLC classified 90.9% (n=10) as positive vs 54.5% (n=6), indicating increased probability of false positive test results with the kit RI when applied to our patient population. Appropriate and specific reference intervals and criteria values result in fewer false-positive and false-negative results which means fewer wrong or missed diagnoses.

• Journal of Korean Society for Quality Management
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• v.23 no.4
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• pp.28-41
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• 1995

We consider the problem of identifying multiple outliers in linear model. The available regression diagnostic methods often do not succeed in detecting multiple outliers because of the masking and swamping effect. Recently, among the various robust estimator of reducing the effect of outliers, LMS(Least Meadian Square) estimator has been to be a suitable method proposed to expose outliers and leverage points. However, as you know it, the data analysis method with LMS estimator is to be taken the median of the squared residuals in the sample which is extracted the sample space. Then this model causes the trouble, for the number of the chosen sample is nCp, i.e. as the size of sample space n is increasing, the number is increasing fastly. And the covariance matrix may be the singular matrix, so that matrix is approching collinearity. Thus we propose a procedure ELMS for the resampling in LMS method and study the size of the effective elementary set in this algorithm.