• Asian Pacific Journal of Cancer Prevention
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• 제17권3호
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• pp.895-904
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• 2016

Breast cancer is the most commonly diagnosed invasive cancer among women. Many factors, both genetic and non-genetic, determine a woman's risk of developing breast cancer and several breast cancer risk prediction models have been proposed. It is vitally important to risk stratify patients as there are now effective preventive strategies available. All women need to be counseled regarding healthy lifestyle recommendations to decrease breast cancer risk. As such, management of these women requires healthcare professionals to be familiar with additional risk factors so that timely recommendations can be made on surveillance/risk-reducing strategies. Breast cancer risk reduction strategies can be better understood by encouraging the women at risk to participate in clinical trials to test new strategies for decreasing the risk. This article reviews the advances in the identification of women at high risk of developing breast cancer and also reviews the strategies available for breast cancer prevention.

• 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.

• 한국인공지능학회지
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• 제6권2호
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• pp.11-15
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• 2018

Breast cancer is one of the leading causes of cancer related death among women. So prediction of overall survival status is important into decided in adjuvant treatment. Deep belief network is a kind of artificial intelligence (AI). We intended to construct prediction model by deep belief network using associated clinicopathologic factors. 103881 cases were found in the Korean Breast Cancer Registry. After preprocessing of data, a total of 15733 cases were enrolled in this study. The median follow-up period was 82.4 months. In univariate analysis for overall survival (OS), the patients with advanced AJCC stage showed relatively high HR (HR=1.216 95% CI: 0.011-289.331, p=0.001). Based on results of univariate and multivariate analysis, input variables for learning model included 17 variables associated with overall survival rate. output was presented in one of two states: event or cencored. Individual sensitivity of training set and test set for predicting overall survival status were 89.6% and 91.2% respectively. And specificity of that were 49.4% and 48.9% respectively. So the accuracy of our study for predicting overall survival status was 82.78%. Prediction model based on Deep belief network appears to be effective in predicting overall survival status and, in particular, is expected to be applicable to decide on adjuvant treatment after surgical treatment.

• 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.

• International journal of advanced smart convergence
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• 제13권3호
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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 Pacific Journal of Cancer Prevention
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• 제14권4호
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• pp.2371-2375
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• 2013

Breast cancer is one of the most common malignancies in women around the world. Among the various hormonal types of breast cancer, those that are estrogen receptor (ER) positive account for the majority. Among the estrogen related receptors, estrogen related receptor ${\alpha}$ is known to have a potential role in breast cancer and is one of the therapeutic target. Hence, prediction of novel ligands interact with estrogen related receptor alpha is therapeutically important. The present study, aims at prediction and analysis of ligands from the KEGG COMPOUND database (containing 10,739 entries) able to interact against estrogen receptor alpha using a similarity search and molecular docking approach.

• Genomics & Informatics
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• 제15권4호
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• pp.156-161
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• 2017

Transcriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specific differential expression between normal and breast cancer cells using public data. We retrieved the values for gene expression levels in breast cancer and matched normal cells from The Cancer Genome Atlas. We divided genes into two classes by paired t test without considering age in the first classification. We carried out a secondary classification of genes for each class into eight groups, based on the patterns of the p-values, which were calculated for each of the three age groups we defined. Through this two-step classification, gene expression was eventually grouped into 16 classes. We showed that this classification method could be applied to establish a more accurate prediction model to diagnose breast cancer by comparing the performance of prediction models with different combinations of genes. We expect that our scheme of classification could be used for other types of cancer data.

• Asian Pacific Journal of Cancer Prevention
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• 제15권14호
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• pp.5883-5888
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• 2014

Background: Breast cancer is the most common cancers in female populations. The exact cause is not known, but is most likely to be a combination of genetic and environmental factors. Log-logistic model (LLM) is applied as a statistical method for predicting survival and it influencing factors. In recent decades, artificial neural network (ANN) models have been increasingly applied to predict survival data. The present research was conducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer (BC) survival. Materials and Methods: A historical cohort study was established with 104 patients suffering from BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas under the receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzed using R statistical software. Results: The AUC for the first, second and third years after diagnosis are 0.918, 0.780 and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statistically higher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between the performance in terms of prediction by ANN and LLM. Conclusions: This study demonstrated that the ability of prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction of survival in field of breast cancer is suggested.

• Asian Pacific Journal of Cancer Prevention
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• 제15권3호
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• pp.1067-1074
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• 2014

In Morocco, breast cancer is the most prevalent cancer in women and a major public health problem. Several Moroccan studies have focused on studying this disease, but more are needed, especially at the genetic and molecular levels. It is therefore interesting to establish the genetic and molecular profile of Moroccan patients with breast cancer. In this paper, we will highlight some pertinent hypotheses that may enhance breast cancer care in Moroccan patients. This review will give a precise description of breast cancer in Morocco and propose some new markers for detection and prediction of breast cancer prognosis.

• Asian Pacific Journal of Cancer Prevention
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• 제15권6호
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• pp.2785-2791
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• 2014

Aims: To analyze time-dependent changes in female breast cancer (BC) mortality in China, forecast the trend in the ensuing 5 years, and provide recommendations for prevention and management. Materials and Methods: Mortality data of breast cancer in China from 1991 to 2011 was used to describe characteristics and distribution, such as the changes of the standardized mortality rate, urban-rural differences and age differences. Trend-surface analysis was used to study the geographical distribution of mortality. In addition, curve estimation, time series modeling, Gray modeling (GM) and joinpoint regression were performed to estimate and predict future trends. Results: In China, the mortality rate of breast cancer has increased yearly since 1991. In addition, our data predicted that the trend will continue to increase in the ensuing 5 years. Rates in urban areas are higher than those in rural areas. Over the past decade, all peak ages for death by breast cancer have been delayed, with the first death peak occurring at 55 to 65 years of age in urban and rural areas. Geographical analysis indicated that mortality rates increased from Southwest to Northeast and from West to East. Conclusions: The standardized mortality rate of breast cancer in China is rising and the upward trend is predicted to continue for the next 5 years. Since this can cause an enormous health impact in China, much better prevention and management of breast cancer is needed. Consequently, disease control centers in China should place more focus on the northeastern, eastern and southeastern parts of China for breast cancer prevention and management, and the key population should be among women between ages 55 to 65, especially those in urban communities.