• 정보처리학회논문지B
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• 제11B권6호
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• pp.717-722
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• 2004

본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted Fuzzy Membership Functions, NNWFM)을 이용하여 Wisconsin breast cancer의 예측을 수행하는 퍼지규칙을 추출하고 있다. NNWFM는 자기적응적(self adaptive)가중 퍼지소속함수를 가지고 주어진 입력 데이터로부터 학습하여 퍼지규칙을 생성하고 이론 기반으로 예측을 수행한다. 신경망 구조의 중간 부분인 하이퍼박스(hyperbox)들은 n개의 대, 중, 소의 가중 퍼지소속함수 집합으로 구성되며, 학습 후 각 집합은 퍼지집합의 bounded sum을 사용하여 다시 하나의 가중 퍼지소속함수로 합성된다. n개의 특징입력(feature input)은 학습된 모든 하이퍼박스에 연결되어 예측 작업을 수행한다. NNWFM으로 추출된 2개의 퍼지규칙은 99.41%의 예측 인식율을 가지며 이는 퍼지규칙의 수와 인식율에 있어 현재 발표된 논문의 결과보다 우수함을 보여준다.

• International Journal of Internet, Broadcasting and Communication
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• 제13권2호
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.113-120
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• 2023

One of the most prevalent disease among women that leads to death is breast cancer. It can be diagnosed by classifying tumors. There are two different types of tumors i.e: malignant and benign tumors. Physicians need a reliable diagnosis procedure to distinguish between these tumors. However, generally it is very difficult to distinguish tumors even by the experts. Thus, automation of diagnostic system is needed for diagnosing tumors. This paper attempts to improve the accuracy of breast cancer detection by utilizing deep learning convolutional neural network (CNN). Experiments are conducted using Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Compared to existing techniques, the used of CNN shows a better result and achieves 99.66%% in term of accuracy.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.196-202
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• 2024

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer's favorable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, and finally the F1 score. In this study hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.

• 한국지능시스템학회논문지
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• 제14권1호
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• pp.46-51
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• 2004

본 논문에서는 선형행렬 부등식을 이용한 TS 퍼지 분류기 설계 방법을 제안한다. TS 퍼지 분류기를 설계하기 위해 퍼지규칙의 후반부 파라메터가 분류기의 성능을 최대로 하도록 동정되어야 한다. 이러한 동정 문제를 해결하기 위해 볼록 최적화 기법이 사용되었다. 후반부 파라메터 동정 문제는 볼록 최적화 문제로 변환되며, 선형행렬 부등식으로 표현된다. 선형행렬 부등식으로 표현된 볼록 최적화 문제는 일반 고유값 문제로 근사화 되며, 일반 고유값 문제를 최적화함으로써 최소의 분류 에러를 가지는 최적의 후반부 파라메터가 결정된다. 제안된 분류기의 성능을 평가하기 위해 IRIS 데이터와 Wisconsin Breast Cancer Database 데이터에 대한 분류기의 성능을 모의 실험을 통해 확인하였다. 마지막으로, 모의 실험 결과 제안된 TS 퍼지 분류기의 성능의 우수성을 확인할 수 있었다.

• 전자공학회논문지C
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• 제35C권10호
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• pp.59-68
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• 1998

데이터 마이닝의 목적은 유용한 패턴을 찾음으로써 데이터를 이해하는데 있으므로, 찾아진 패턴은 정확할뿐 아니라 이해하기 쉬워야한다. 따라서 정확하고 이해하기 쉬운 패턴을 추출하는 데이터 마이닝에 대한 연구가 필요하다. 본 논문에서는 퍼지 결정 트리를 이용한 효과적인 데이터 마이닝 알고리즘을 제안한다. 제안된 알고리즘은 ID3, C4.5와 같은 결정 트리 알고리즘의 이해하기 쉬운 장점과 퍼지의 표현력을 결합하여 간결하고 이해하기 쉬운 규칙을 생성한다. 제안된 알고리즘은 히스토그램에 기반하여 퍼지 소속함수를 생성하는 단계와 생성된 소속 함수를 이용하여 퍼지 결정 트리를 구성하는 두 단계로 이루어진다. 또한 제안된 방법의 타당성을 검증하기 위하여 표준적인 패턴 분류 벤치마크 데이터인 Iris 데이터와 Wisconsin Breast Cancer 데이터에 대한 실험 결과를 보인다.

• Asian Pacific Journal of Cancer Prevention
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• 제15권18호
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• pp.7775-7780
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• 2014

This paper addresses cancer prediction based on radial basis function neural network optimized by particle swarm optimization. Today, cancer hazard to people is increasing, and it is often difficult to cure cancer. The occurrence of cancer can be predicted by the method of the computer so that people can take timely and effective measures to prevent the occurrence of cancer. In this paper, the occurrence of cancer is predicted by the means of Radial Basis Function Neural Network Optimized by Particle Swarm Optimization. The neural network parameters to be optimized include the weight vector between network hidden layer and output layer, and the threshold of output layer neurons. The experimental data were obtained from the Wisconsin breast cancer database. A total of 12 experiments were done by setting 12 different sets of experimental result reliability. The findings show that the method can improve the accuracy, reliability and stability of cancer prediction greatly and effectively.

• 종양간호연구
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• 제12권2호
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• pp.175-185
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• 2012

Purpose: This micro-ethnographic study aimed to understand coping experiences of Korean-American (K-A) women after diagnosis with breast cancer due to a hereditary gene mutation. Methods: Participatory observation and in-depth interviews were performed at one breast cancer screening center in Southern California, in 2005 with eleven first generation K-A immigrant women. All transcribed interviews and field notes were analyzed using ethnographic methodology. Results: K-A women's experience varied based on acculturation risk factors including: limited English speaking ability; disrupted family relationships, individualistic family values, or intergenerational communication barriers; lack of Korean speaking nurses; and Korean physicians' who lacked knowledge about hereditary breast cancer risk. These risk factors led to isolation, loneliness, lack of emotional and social support. In comparison to Korean homeland women in a similar medical situation, these K-A immigrants felt disconnected from the healthcare system, family support and social resources which increased their struggling and impeded coping during their survivorship journey. These women were not able to access self-support groups, nor the valuable resources of nurse navigator programs. Conclusion: Professional oncology associations for nurses and physicians have a moral obligation to support and promote knowledge of hereditary cancer risk and self-help groups for non-native speaking immigrants.

• 응용통계연구
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• 제25권6호
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• pp.947-958
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• 2012

When modeling event times in biomedical studies, the outcome might be incompletely observed. In this paper, we assume that the outcome is recorded as current status failure time data. Despite well-developed literature the routine practical use of many current status data modeling methods remains infrequent due to the lack of specialized statistical software, the difficulty to assess model goodness-of-fit, as well as the possible loss of information caused by covariate grouping or discretization. We propose a model based on pseudo-observations that is convenient to implement and that allows for flexibility in the choice of the outcome. Parameter estimates are obtained based on generalized estimating equations. Examples from studies in bile duct hyperplasia and breast cancer in conjunction with simulated data illustrate the practical advantages of this model.

• 한국지능시스템학회논문지
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• 제15권5호
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• pp.599-604
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• 2005

본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted Fuzzy Membership Functions, NEWFM)을 이용하여 위스콘신 유방암(Wisconsin breast cancer)의 진단을 수행하는 퍼지규칙을 추출하고, 비중복면적 분산 측정법을 사용하여 특징입력수를 최소로하는 방안을 제안하고 있다. NEWFM 구조의 중간 부분인 하이퍼박스(hyperbox)들은n 개의 대, 중, 소로 구성된 가중 퍼지소속함수 집합으로 구성되며, 학습 후 각 집합의 대, 중, 소로 구성된 가중 퍼지소속함수는 퍼지집합의 경계합(bounded sum)을 사용하여 다시 하나의 가중 퍼지소속함수로 합성(BSWFM) 된다. n 개의 특징입력(feature input)은 학습된 모든 하이퍼박스에 연결되어 예측 작업을 수행한다. 여기에 비중복면적 분산 측정법을 적용하여 중요도가 낮은 특징입력을 제거하면서 최소의 m 개 특징입력만을 사용한 하이퍼박스로 단순화시킨다. 이러한 방법으로 위스콘신 유방암의 9개의 특징입력 중 4개를 사용하여 NEWFM으로 추출된 2개의 퍼지규칙은 99.71%의 예측 인식율을 가지며 이는 퍼지규칙의 수와 인식율에 있어 현재 발표된 논문의 결과보다 우수함을 보여준다.