scholarly journals Predicting Breast Cancer Classification Using Various Machine Learning Classification Algorithm

2020 ◽  
Vol 6 (12) ◽  
pp. 282-285
Author(s):  
Nishant Bansal Nidhi Sengar and Amita Goe
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
High Performance ◽  
Breast Cancer Diagnosis ◽  
Second Primary ◽  
Machine Learning Classification ◽  
University Of Wisconsin ◽  
Sensitivity Specificity ◽  
The University

Cancer diagnosis is one among the foremost studied problems within the medical domain. Several researchers have focused so as to enhance performance and achieve to get satisfactory results. Breast cancer[1] represents the second primary explanation for cancer deaths in women today and has become the foremost common cancer among women both within the developed and therefore the developing world in the last years. Breast cancer diagnosis is used to categorize the patients among benign (lacks ability to invade neighbouring tissue) from malignant (ability to invade neighbouring tissue) categories. In this study, the diagnosis of breast cancer from mammograms is complemented by using various classification techniques. In artificial intelligence, machine learning is a discipline which allows to the machine to evolve through a process. Machine learning[2] is widely utilized in bio-informatics and particularly in carcinoma diagnosis. This paper explores the various data processing approaches using Classification which may be applied on carcinoma data to create deep predictions. Besides this, this study predicts the simplest Model yielding high performance by evaluating dataset on various classifiers.[4-8] The results that are obtained through the research are assessed on various parameters like Accuracy, RMSE Error, Sensitivity, Specificity etc. Our work is going to be performed on the WBCD database (Wisconsin carcinoma Database) [12]obtained by the university of Wisconsin Hospital.

scholarly journals Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xiao-hong Mao ◽  
Qiang Ye ◽  
Guo-bing Zhang ◽  
Jin-ying Jiang ◽  
Hong-ying Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
High Sensitivity ◽  
Breast Cancer Diagnosis ◽  
Diagnostic Value ◽  
Methylation Pattern ◽  
Clinical Value ◽  
Roc Curve Analysis ◽  
Differentially Methylated Genes ◽  
Sensitivity Specificity

Abstract Background Aberrant DNA methylation is significantly associated with breast cancer. Methods In this study, we aimed to determine novel methylation biomarkers using a bioinformatics analysis approach that could have clinical value for breast cancer diagnosis and prognosis. Firstly, differentially methylated DNA patterns were detected in breast cancer samples by comparing publicly available datasets (GSE72245 and GSE88883). Methylation levels in 7 selected methylation biomarkers were also estimated using the online tool UALCAN. Next, we evaluated the diagnostic value of these selected biomarkers in two independent cohorts, as well as in two mixed cohorts, through ROC curve analysis. Finally, prognostic value of the selected methylation biomarkers was evaluated breast cancer by the Kaplan-Meier plot analysis. Results In this study, a total of 23 significant differentially methylated sites, corresponding to 9 different genes, were identified in breast cancer datasets. Among the 9 identified genes, ADCY4, CPXM1, DNM3, GNG4, MAST1, mir129-2, PRDM14, and ZNF177 were hypermethylated. Importantly, individual value of each selected methylation gene was greater than 0.9, whereas predictive value for all genes combined was 0.9998. We also found the AUC for the combined signature of 7 genes (ADCY4, CPXM1, DNM3, GNG4, MAST1, PRDM14, ZNF177) was 0.9998 [95% CI 0.9994–1], and the AUC for the combined signature of 3 genes (MAST1, PRDM14, and ZNF177) was 0.9991 [95% CI 0.9976–1]. Results from additional validation analyses showed that MAST1, PRDM14, and ZNF177 had high sensitivity, specificity, and accuracy for breast cancer diagnosis. Lastly, patient survival analysis revealed that high expression of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 were significantly associated with better overall survival. Conclusions Methylation pattern of MAST1, PRDM14, and ZNF177 may represent new diagnostic biomarkers for breast cancer, while methylation of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 may hold prognostic potential for breast cancer.

scholarly journals Classifications of Breast Cancer Diagnosis using Machine Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Breast Cancer Diagnosis ◽  
Performance Comparison ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
K Nearest Neighbors ◽  
Cancer Dataset ◽  
Machine Learning Classification

Breast Cancer (BC) is amongst the most common and leading causes of deaths in women throughout the world. Recently, classification and data analysis tools are being widely used in the medical field for diagnosis, prognosis and decision making to help lower down the risks of people dying or suffering from diseases. Advanced machine learning methods have proven to give hope for patients as this has helped the doctors in early detection of diseases like Breast Cancer that can be fatal, in support with providing accurate outcomes. However, the results highly depend on the techniques used for feature selection and classification which will produce a strong machine learning model. In this paper, a performance comparison is conducted using four classifiers which are Multilayer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest on the Wisconsin Breast Cancer dataset to spot the most effective predictors. The main goal is to apply best machine learning classification methods to predict the Breast Cancer as benign or malignant using terms such as accuracy, f-measure, precision and recall. Experimental results show that Random forest is proven to achieve the highest accuracy of 99.26% on this dataset and features, while SVM and KNN show 97.78% and 97.04% accuracy respectively. MLP shows the least accuracy of 94.07%. All the experiments are conducted using RStudio as the data mining tool platform.

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