scholarly journals Breast Cancer Prediction Using a Hybrid Data Mining Model

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Elham Bahmani ◽  
Mojtaba Jamshidi ◽  
Abdusalam Shaltooki
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Hybrid Model ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Cancer Type ◽  
Rbf Network ◽  
Proposed Model ◽  
Automatic Tool ◽  
Breast Cancer Type

Today, with the emergence of data mining technology and access to useful data, valuable information in different areas can be explored. Data mining uses machine learning algorithms to extract useful relationships and knowledge from a large amount of data and offers an automatic tool for various predictions and classifications. One of the most common applications of data mining in medicine and health-care is to predict different types of breast cancer which has attracted the attention of many scientists. In this paper, a hybrid model employing three algorithms of Naive Bayes Network, RBF Network, and K-means clustering is presented to predict breast cancer type. In the proposed model, the voting approach is used to combine the results obtained from the above three algorithms. Dataset used in this study is called Breast Cancer Wisconsin taken from data sources of UCI. The proposed model is implemented in MATLAB and its efficiency in predicting breast cancer type is evaluated on Breast Cancer Wisconsin dataset. Results show that the proposed hybrid model achieves an accuracy of 99% and mean absolute error of 0.019 which is superior over other models.

scholarly journals Breast Cancer Prediction using CNN and Machine Learning Algorithms with Comparative Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 876-883
Author(s):  
Santhosh Voruganti
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Mining ◽  
Early Stage ◽  
Learning Algorithms ◽  
Malignant Tumour ◽  
Machine Learning Algorithms ◽  
Cancer Type ◽  
Cancer Disease ◽  
Cancer Breast

At present world, Breast cancer is a second main cause of cancer death in women after lung cancer. Breast cancer occurs when some breast cells begin to raise abnormally. It can arise in any portion of the Breast and it can be prevented if the treatment is started at the early stage of the Breast cancer. Breast cancer is a malignant tumour i.e. a collection of cancer cells arising from the cells of the breast Treatment of breast cancer relies on the cancer type and its stage. Mainly this paper focused on diagnosing the Breast cancer disease using various classification algorithm with the help of data mining tools. Data mining of the intelligent accumulated from previously disease detected patients opened up a new aspect of medical progression In this paper, the focus has been prediction of breast cancer using various machine learning algorithms and visualizing the performances of each algorithm. This paper makes use of a dataset that contains numerical values about the clump thickness, uniformity of the cell for prediction using Multi Layer Perceptron, K-NN, Random Forest, Logistic Regression. Moreover, this also uses a dataset of tissue images for the prediction using Convolution Neural Network. Later, the accuracies of each algorithm is calculated along with the precision, recall, f-score, ROC for each algorithm.

scholarly journals Prediction of benign and malignant breast cancer using data mining techniques

2018 ◽  
Vol 12 (2) ◽  
pp. 119-126 ◽  
Author(s):  
Vikas Chaurasia ◽  
Saurabh Pal ◽  
BB Tiwari
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Low Income ◽  
Prediction Models ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Low Income Countries ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Rbf Network

Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.

scholarly journals Prominent Prognostic Factors in Aggressive Breast Cancer: A Review

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Kazem Nejati ◽  
Sedigheh Fekri Aval ◽  
Mohammadreza Alivand ◽  
Abolfazl Akbarzadeh ◽  
AmirAhmad Arabzadeh
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Growth Factor Receptor ◽  
Progression Free Survival ◽  
Lymph Node Status ◽  
Cancer Type ◽  
Ki 67 ◽  
Increased Risk ◽  
Aggressive Breast Cancer ◽  
Breast Cancer Type

Context: Breast cancer (BC) is the most common cancer in women worldwide. Hereditary susceptibility created by mutations in autosomal dominant genes is responsible for 5 to 10% of all BC cases in women. Recent studies have identified genes associated with increased risk for aggressive BC, providing the basis for better risk management. Evidence Acquisition: The latest information in National Center for Biotechnology Information (NCBI), Google Scholar, ScienceDirect, and Scopus were the main databases for finding articles. A combination of keywords of ‘metastasis’, ‘invasion’, ‘aggressive breast cancer’, ‘prognostic factor’, ‘mutation’, and ‘cancer treatment’ was searched in the databases to identify related articles. Titles and abstracts of the articles were studied to choose the right articles. Results: Mutations in breast cancer type 1 susceptibility protein (BRCA1) and breast cancer type 2 susceptibility protein (BRCA2) genes are two central players related to the high risk of BC. Mutation in tumor protein p53 (TP53) is another important mutation that leads to triple-negative BC. Although the majority of BC types are not associated with high-throughput mutant genes such as BRCA1, BRCA2, and TP53, they are associated with low-throughput genes, including DNA repair protein Rad50 (RAD50), Nijmegen breakage syndrome gene (NBS1), checkpoint kinase 2 (CHEK2), BRCA1-interacting protein 1 (BRIP1), E-cadherin gene (CDH1) and PALB2, UCHL1, aldehydedehydrogenase1A3 (ALDH1A3), androgen receptor (AR), 5-bisphosphate 3-kinase (PIK3CA), phosphatidylinositol-4, and luminal gene expression that are generally mutated in the global population. High tumor mutational burden (TMB) was associated with improved progression-free survival. Conclusions: The lymph node status, early tumor size, ER, PR, human epidermal growth factor receptor-2 (HER2), and Ki-67 are conventional prognostic factors for BC. However, these factors cannot exactly predict the aggressive behavior of BC. Hence, in this review, we discussed new prognostic factors of aggressive BCs that are useful for the treatment of patients with BC.

scholarly journals Structural basis of homologous recombination

2019 ◽  
Vol 77 (1) ◽  
pp. 3-18 ◽  
Author(s):  
Yueru Sun ◽  
Thomas J. McCorvie ◽  
Luke A. Yates ◽  
Xiaodong Zhang
Keyword(s):  
Breast Cancer ◽  
Electron Microscopy ◽  
Homologous Recombination ◽  
Ataxia Telangiectasia ◽  
Structural Information ◽  
Homology Search ◽  
Structural Basis ◽  
Cancer Type ◽  
Dna Double Strand Breaks ◽  
Breast Cancer Type

AbstractHomologous recombination (HR) is a pathway to faithfully repair DNA double-strand breaks (DSBs). At the core of this pathway is a DNA recombinase, which, as a nucleoprotein filament on ssDNA, pairs with homologous DNA as a template to repair the damaged site. In eukaryotes Rad51 is the recombinase capable of carrying out essential steps including strand invasion, homology search on the sister chromatid and strand exchange. Importantly, a tightly regulated process involving many protein factors has evolved to ensure proper localisation of this DNA repair machinery and its correct timing within the cell cycle. Dysregulation of any of the proteins involved can result in unchecked DNA damage, leading to uncontrolled cell division and cancer. Indeed, many are tumour suppressors and are key targets in the development of new cancer therapies. Over the past 40 years, our structural and mechanistic understanding of homologous recombination has steadily increased with notable recent advancements due to the advances in single particle cryo electron microscopy. These have resulted in higher resolution structural models of the signalling proteins ATM (ataxia telangiectasia mutated), and ATR (ataxia telangiectasia and Rad3-related protein), along with various structures of Rad51. However, structural information of the other major players involved, such as BRCA1 (breast cancer type 1 susceptibility protein) and BRCA2 (breast cancer type 2 susceptibility protein), has been limited to crystal structures of isolated domains and low-resolution electron microscopy reconstructions of the full-length proteins. Here we summarise the current structural understanding of homologous recombination, focusing on key proteins in recruitment and signalling events as well as the mediators for the Rad51 recombinase.

A COMPARITIVE ANALYSIS AND TIME SERIES FORECASTING OF MONTHLY STREAM FLOW DATA USING HYBRID MODEL

2015 ◽  
Vol 76 (13) ◽  
Author(s):  
Siraj Muhammed Pandhiani ◽  
Ani Shabri
Keyword(s):  
Hybrid Model ◽  
Stream Flow ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Flow Data ◽  
Indus River ◽  
Proposed Model

In this study, new hybrid model is developed by integrating two models, the discrete wavelet transform and least square support vector machine (WLSSVM) model. The hybrid model is then used to measure for monthly stream flow forecasting for two major rivers in Pakistan. The monthly stream flow forecasting results are obtained by applying this model individually to forecast the rivers flow data of the Indus River and Neelum Rivers. The root mean square error (RMSE), mean absolute error (MAE) and the correlation (R) statistics are used for evaluating the accuracy of the WLSSVM, the proposed model. The results are compared with the results obtained through LSSVM. The outcome of such comparison shows that WLSSVM model is more accurate and efficient than LSSVM.

scholarly journals Immunohistochemistry for the detection of BRCA1 and BRCA2 proteins in patients with ovarian cancer: a systematic review

2019 ◽  
Vol 73 (4) ◽  
pp. 191-196 ◽  
Author(s):  
Lorena Alves Teixeira ◽  
Francisco Jose Candido dos Reis
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Ovarian Cancer ◽  
Cancer Type ◽  
Loss Of Function ◽  
Brca1 And Brca2 ◽  
Brca1 Protein ◽  
Current Usage ◽  
Breast Cancer Type

BackgroundLoss of function in either breast cancer type 1 susceptibility protein (BRCA1) or breast cancer type 2 susceptibility protein (BRCA2) is a major risk factor for epithelial ovarian cancer (EOC) development. BRCA1 or BRCA2 deficiencies are associated with short-term prognosis and might have importance for the treatment of women with the disease. However, the screening of all possible mechanisms of dysfunction is expensive, time-consuming and difficult to apply in clinical practice. On the other hand, immunohistochemistry (IHC) is a simple and reliable method to access the expression of several proteins in tumour tissues.Materials and methodsThis systematic review aims to evaluate the current usage of IHC to detect BRCA1 and BRCA2 deficiencies in EOC. We searched and evaluated all primary literature on the use of IHC for evaluating BRCA1 and BRCA2 proteins expression in EOC. The main concepts for the search were: ovarian neoplasms, IHC, BRCA1 and BRCA2.ResultsForty-four studies from 925 unique titles were included. A total of 4206 tumour samples were evaluated for BRCA1 and 1041 for BRCA2 expression. Twelve BRCA1 primary antibodies were used in 41 studies, and the most common was the MS110 clone (75.6%). Seven BRCA2 primary antibodies were used in ten studies. Using the cut-off of 10%, 47.0% of EOCs are associated with loss of BRCA1 and 34.5% with the loss of BRCA2 expression.ConclusionIHC was effective to detect loss of BRCA1 protein expression in EOC; however, data on BRCA2 expression were heterogeneous and difficult to interpret.

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