• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• Korean Medical Education Review
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• v.19 no.3
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• pp.158-168
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• 2017

There is a more urgent call for educational methods of machine learning in medical education, and therefore, new approaches of teaching and researching machine learning in medicine are needed. This paper presents a case using machine learning through text analysis. Topic modeling of news articles with the keyword 'asbestos' were examined. Two hypotheses were tested using this method, and the process of machine learning of texts is illustrated through this example. Using an automated text analysis method, all the news articles published from January 1, 1990 to November 15, 2016 in South Korea which included 'asbestos' in the title and the body were collected by web scraping. Differences in topics were analyzed by structured topic modelling (STM) and compared by press companies and periods. More articles were found in liberal media outlets. Differences were found in the number and types of topics in the articles according to the partisanship and period. STM showed that the conservative press views asbestos as a personal problem, while the progressive press views asbestos as a social problem. A divergence in the perspective for emphasizing the issues of asbestos between the conservative press and progressive press was also found. Social perspective influences the main topics of news stories. Thus, the patients' uneasiness and pain are not presented by both sources of media. In addition, topics differ between news media sources based on partisanship, and therefore cause divergence in readers' framing. The method of text analysis and its strengths and weaknesses are explained, and an application for the teaching and researching of machine learning in medical education using the methodology of text analysis is considered. An educational method of machine learning in medical education is urgent for future generations.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.97-99
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• 2002

A radiation incident took place during treatment on MEDNIF Tele cobalt-60 therapy machine in B.P.KOIRALA MEMORIAL CANCER HOSPITAL in Bharatpur, Nepal. This Chinese made machine has activity of 6240 Curies of cobalt -60. This machine has fulfilled safety requirements. ICRP recommendations, safety rules are followed and practiced. The source was struck up during treatment and a technician was exposed to equivalent dose of 13.75 mSv. recorded by Personal film badge. Risks of workers are comparable to other safe industries. All exposures shall be kept as low as reasonably possible. The higher level of safety is achieved only when every one is dedicated to common goal. A lesson is learnt for future. Good practice is essential but not sufficient. A high demand for tele Cobalt therapy convinced management to replace Mednif machine with a new efficient Elite Tele Cobalt theratron Machine.

• Journal of radiological science and technology
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• v.38 no.3
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• pp.213-220
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• 2015

This research was study the accuracy of three-dimensional stereotactic breast biopsy, using a core Needle Biopsy and to assess the accuracy of Stereotactic biopsy and Sono guided biopsy. Using Stereotactic QC phantom to measure the accuracy of the 3D sterotactic machine. CT Scan and equipment obtained in the measured X, Y, Z and compares the accuracy of the length. Using Agar power phantom compare the accuracy of the 3D sterotactic machine and 2D ultrasound machine. Z axis measured by the equipment to compare the accuracy and reliability. Check the accuracy by using visual inspection and Specimen Medical application phantom. The accuracy of the 3D sterotactic machine measured by Stereotactic QC phantom was 100%. Accuracy as compared to CT, all of X, Y, Z axis is p > 0.05. The accuracy of the two devices was 100% as measured by Agar powder phantom. There was no difference between t he t wo d evices as C T and p > 0.05. 3D sterotactic machine of the ICC was 0.954, 2D ultrasound machine was 0.785. 2D ultrasound machine was different according to the inspector. Medical application phantom experiments in 3D sterotactic machine could not find the Sliced boneless ham. 2D ultrasound machine has not been able to find a small chalk powder group. The reproducibility of the three-dimensional stereotactic breast biopsy was better than effect of Sono guided biopsy.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.151-157
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• 2011

Tattooing is a performance for decorative and cosmetic marking by placing permanent ink into the skin. As the cultural meaning of tattoo in Korea is changing, the tattoo machines are widely spread n permanent cosmetic market. Though the use of the tattoo machine was increased, the evaluation standards of tattoo machine were not existed. Korea Food and Drug Association regulated the electrical and mechanical safety standards which were founded on the IEC 601-1 second edition. Also they regulated he biological safety standards which were derived from the ISO 10993 series, however, these general valuations of common medical device were insufficient for evaluating tattoo machine. We developed the standards of tattoo machine for safety and performance evaluation for tattoo machine by preliminary hazard analysis in ISO 14971. The evaluation criteria of tattoo machines are focused on the mechanical invasion. We suggested the additional evaluation items of the needle speed, length, vibration with general valuation criteria of common medical device. We anticipate that this research may be a primary stage to figure a standard regulation and evaluation for tattoo machine.

• Progress in Medical Physics
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• v.28 no.3
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• pp.129-133
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• 2017

The machine log files recorded by a scanning control unit in proton beam therapy system have been studied to be used as a quality assurance method of scanning beam deliveries. The accuracy of the data in the log files have been evaluated with a standard calibration beam scan pattern. The proton beam scan pattern has been delivered on a gafchromic film located at the isocenter plane of the proton beam treatment nozzle and found to agree within ${\pm}1.0mm$. The machine data accumulated for the scanning beam proton therapy of five different cases have been analyzed using a statistical method to estimate any systematic error in the data. The high-precision scanning beam log files in line scanning proton therapy system have been validated to be used for off-line scanning beam monitoring and thus as a patient-specific quality assurance method. The use of the machine log files for patient-specific quality assurance would simplify the quality assurance procedure with accurate scanning beam data.

• Journal of radiological science and technology
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• v.42 no.1
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• pp.9-17
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• 2019

Although digital mammography is a representative method for breast cancer detection. It has a limitation in detecting and classifying breast tumor due to superimposed structures. Machine learning, which is a part of artificial intelligence fields, is a method for analysing a large amount of data using complex algorithms, recognizing patterns and making prediction. In this study, we proposed a technique to improve the diagnostic accuracy of energy-selective mammography by training data using the machine learning algorithm and using dual-energy measurements. A dual-energy images obtained from a photon-counting detector were used for the input data of machine learning algorithms, and we analyzed the accuracy of predicted tumor thickness for verifying the machine learning algorithms. The results showed that the classification accuracy of tumor thickness was above 95% and was improved with an increase of imput data. Therefore, we expect that the diagnostic accuracy of energy-selective mammography can be improved by using machine learning.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.1-6
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• 2021

Recently, pre-trained convolutional neural network CNNs have been widely used and applied for medical image classification. These models can utilised in three different ways, for feature extraction, to use the architecture of the pre-trained model and to train some layers while freezing others. In this study, the ResNet18 pre-trained CNNs model is used for feature extraction, followed by the support vector machine for multiple classes to classify medical images from multi-classes, which is used as the main classifier. Our proposed classification method was implemented on Kvasir and PH2 medical image datasets. The overall accuracy was 93.38% and 91.67% for Kvasir and PH2 datasets, respectively. The classification results and performance of our proposed method outperformed some of the related similar methods in this area of study.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.186-191
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• 2023

Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches.

• Journal of the korean veterinary medical association
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• v.38 no.2
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• pp.164-167
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• 2002