• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.655-656
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• 2015

In this paper, diagnosis for 85 high voltage induction motors which have operated for more than 20 years in 18 wide area water supply offices were applied and the results of diagnosis were analysed. Furthermore, main factors that would be affecting rotor defects were selected and correlations between dependent variables which was magnitude for sideband frequency on current during operation and independent variables such as starting characteristic, operating time, number of operation, load factor, maker, rotation speed, capacity were analysed. It was clear that factors including starting characteristic, number of operation, maker, rotation speed caused break by correlation analysis. From this, regression equation was deduced through regression analysis. Based on suggested regression equation, it is applied usefully that we can estimate the condition of rotor without onsite diagnosis and plan the schedule of diagnosis.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.61-68
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• 2024

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.

• The Journal of Korean Medicine
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• v.41 no.3
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• pp.1-8
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• 2020

Objectives: This paper aims to investigate the Donguibogam-based pattern diagnosis by applying natural language processing and machine learning. Methods: A database has been constructed by gathering symptoms and pattern diagnosis from Donguibogam. The symptom sentences were tokenized with nouns, verbs, and adjectives with natural language processing tool. To apply symptom sentences into machine learning, Word2Vec model has been established for converting words into numeric vectors. Using the pair of symptom's vector and pattern diagnosis, a pattern prediction model has been trained through Logistic Regression. Results: The Word2Vec model's maximum performance was obtained by optimizing Word2Vec's primary parameters -the number of iterations, the vector's dimensions, and window size. The obtained pattern diagnosis regression model showed 75% (chance level 16.7%) accuracy for the prediction of Six-Qi pattern diagnosis. Conclusions: In this study, we developed pattern diagnosis prediction model based on the symptom and pattern diagnosis from Donguibogam. The prediction accuracy could be increased by the collection of data through future expansions of oriental medicine classics.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.173-191
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• 1999

In this paper we suggest the visualized regression diagnosis tool. The tool is developed by Hangul Delphi on the basis of windows, so users can easily make use of this tool though they do not have the expert knowledge about statistics and computer. Especially, to apply this tool to teaching regression analysis or data analysis, we offer various residual plots in the tool and show the results of analysis graphically.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.393-402
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• 2013

The Mahalanobis-Taguchi System (MTS) is a diagnostic and predictive method for multivariate data. In the MTS, the Mahalanobis space (MS) of reference group is obtained using the standardized variables of normal data. The Mahalanobis space can be used for multi-class classification. Once this MS is established, the useful set of variables is identified to assist in the model analysis or diagnosis using orthogonal arrays and signal-to-noise ratios. And other several techniques have already been used for classification, such as linear discriminant analysis and logistic regression, decision trees, neural networks, etc. The goal of this case study is to compare the ability of the Mahalanobis-Taguchi System and logistic regression using a data set.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.4
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• pp.135-141
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• 2000

The major role of temperature sensors in thermal error compensation system of machine tools is improving machining accuracy by supplying reliable temperature data on the machine structure. This paper presents a new method for fault diagnosis of temperature sensors and recovery of faulted data to establish the reliability of thermal error compensation system. The detection of fault and its location is based on the correlation coefficients among temperature data from the sensors. The multiple linear regression model which is prepared using complete normal data is also used fur the recovery of faulted data. The effectiveness of this method was tested by comparing the computer simulation results and measured data in a CNC machining center.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.80-88
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• 2024

Breast cancer remains a significant global health burden, necessitating accurate and timely detection for improved patient outcomes. Machine learning techniques have demonstrated remarkable potential in assisting breast cancer diagnosis by learning complex patterns from multi-modal patient data. This study comprehensively evaluates several popular machine learning models, including logistic regression, decision trees, random forests, support vector machines (SVMs), naive Bayes, k-nearest neighbors (KNN), XGBoost, and ensemble methods for breast cancer prediction using the Wisconsin Breast Cancer Dataset (WBCD). Through rigorous benchmarking across metrics like accuracy, precision, recall, F1-score, and area under the ROC curve (AUC), we identify the naive Bayes classifier as the top-performing model, achieving an accuracy of 0.974, F1-score of 0.979, and highest AUC of 0.988. Other strong performers include logistic regression, random forests, and XGBoost, with AUC values exceeding 0.95. Our findings showcase the significant potential of machine learning, particularly the robust naive Bayes algorithm, to provide highly accurate and reliable breast cancer screening from fine needle aspirate (FNA) samples, ultimately enabling earlier intervention and optimized treatment strategies.

• Asian Oncology Nursing
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• v.12 no.2
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• pp.156-165
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• 2012

Purpose: This study was to identify predictors of sexual function in gynecologic cancer patients. Methods: The participants were 154 patients treated at a university medical center in A city, Korea. The data collection was performed through a structured questionnaire from July to December, 2010. The instruments used in this study were Female Sexual Function Index (FSFI) perceived health status scale, Eastern Cooperative Oncology Group (ECOG) performance status, body image, and depression. Data were analyzed using descriptive statistics, Mann-Whitney test, Kruskal-Wallis test and stepwise multiple regression with the SPSS 18.0. Results: The mean score of perceived health status was 8.42 and sexual function was 8.42. The lowest score among sexual function was lubrication. The scores of sexual function was significantly different by age, job, marital status, period after diagnosis of cancer and diagnosis. There were significant correlations between sexual function, perceived health status, ECOG performance, body image and depression. In multiple regression analysis, predictors were identified as ECOG performance, age, diagnosis and period after diagnosis of cancer (Adj.$R^2$=.28). The most powerful predictor of female sexual function was ECOG performance (19.0%). Conclusion: The above findings indicate that it is necessary to develop a more effective and personalized sexual function improvement program for gynecologic cancer patient.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.4
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• pp.1627-1631
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• 2015

Background: Stage at diagnosis is one of the most important prognostic factors of breast cancer survival. Because in the breast cancer case this may vary with socioeconomic characteristics, this study was performed to recognize the relationship between demographic and socioeconomic factors with stage at diagnosis in Iran. Materials and Methods: This cross-sectional, descriptive study conducted on 526 patients suffering from breast cancer and registered in Cancer Research Center of Shahid Beheshti University of Medical Sciences from 2008 to 2013. A reliable and valid questionnaire about family levels of socioeconomic status filled in by interviewing the patients via phone. For analyzing the data, Multinomial logistic regression, Kendal tau-b correlation coefficient and Contingency Coefficient tests were executed by SPSS22. Economic status, educational attainment of patient and household head and/or a combination of these were considered as parameters for socioeconomic status. First, the relationship between stage at diagnosis and demographic and socioeconomic status was assessed in univariate analysis then these relationships assessed in two different models of multinomial logistic regression. Results: The mean age of the patients was 48.3 (SD=11.4). According to the results of this study, there were significant relationships between stage at diagnosis of breast cancer with patient education (p=0.011), living place (p=0.044) and combined socioeconomic status (p=0.024). These relationships persisted in multiple multinomial logistic regressions. Other variables, however, had no significant correlation. Conclusions: Patient education, combined socioeconomic status and living place are important variables in stage at diagnosis of breast cancer in Iranian women. Interventions have to be applied with the aim of raising women's accessibility to diagnostic and medical facilities and also awareness in order to reducing delay in referring. In addition, covering breast cancer screening services by insurance is recommended.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.264-267
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• 1998

We are dealing with the preliminary diagnosis from the information of headache interview chart. We quantify the qualitative information based on the interview chart by dual scaling. Prototype of fuzzy diagnostic sets and the neural linear regression methods are established with these quantified data, These new methods can be used to classify new patient's tone of diseases with certain degrees of belief and its concerned symptoms. We call these procedures as neural Fuzzy Differential Diagnosis of Headache (NFDDH-1). Also we investigate three measures to medical diagnosis, where relations between symptoms and diseases are described by intutionistic fuzzy set (IFS) data. Two measures are described by nin-max and max-min IFS operators, respectively. Another measure is the similarity degree, i.e., IFS distance between patient's symptoms and prototypes of diseases. We consider some reasonable criteria for three measures in order to determine the label of headache, We will establish hree measures in NFDDH-2 and combine two packages as NFDDH