Classification of Breast Cancer and Breast Neoplasm Scenarios Based on Machine Learning and Sequence Features from lncRNAs–miRNAs-Diseases Associations

2021 ◽  
Author(s):  
Juan Gutiérrez-Cárdenas ◽  
Zenghui Wang
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Breast Neoplasm ◽  
Sequence Features

scholarly journals Breast Cancer Type Classification Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 61
Author(s):  
Jiande Wu ◽  
Chindo Hicks
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Genomic Research ◽  
Support Vector ◽  
Cancer Type ◽  
Classification Models

Background: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. Methods: We performed analysis of RNA-Sequence data from 110 triple negative and 992 non-triple negative breast cancer tumor samples from The Cancer Genome Atlas to select the features (genes) used in the development and validation of the classification models. We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the models for classifying the two types of breast cancer. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets. Results: Among the four ML algorithms evaluated, the Support Vector Machine algorithm was able to classify breast cancer more accurately into triple negative and non-triple negative breast cancer and had less misclassification errors than the other three algorithms evaluated. Conclusions: The prediction results show that ML algorithms are efficient and can be used for classification of breast cancer into triple negative and non-triple negative breast cancer types.

scholarly journals Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

2012 ◽  
Vol 03 (06) ◽  
pp. 1020-1028 ◽  
Author(s):  
Edén A. Alanís-Reyes ◽  
José L. Hernández-Cruz ◽  
Jesús S. Cepeda ◽  
Camila Castro ◽  
Hugo Terashima-Marín ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Computer Aided Detection ◽  
Learning Techniques ◽  
Computer Aided ◽  
Microcalcification Clusters

Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines

2020 ◽  
Vol 16 (6) ◽  
pp. 703-710 ◽  
Author(s):  
Jebasonia Jebamony ◽  
Dheeba Jacob
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Misclassification Rate ◽  
Support Vector ◽  
Mass Detection ◽  
Training Process ◽  
Vector Machines

Background: Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. Objective: The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. Methods: The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Results: Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Conclusion: Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

scholarly journals Machine Learning Algorithms: Diagnosing Breast Cancer

2019 ◽  
Vol 8 (2S6) ◽  
pp. 849-851
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Benign Tumor ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Cancer Breast ◽  
Cancer Tumor ◽  
The Common

Breast Cancer has become one of the common diseases not only in women but also in few men. According to research, the demise rate of females has increased mainly because of Breast Cancer tumor. One out of every eight women and one out of every thousand men are diagnosed with breast cancer. Breast cancer tumors are mainly classified into two types: Benign tumor which is a non-cancerous tumor and other one is malignant tumor which is a cancerous tumor. In order to know which type of tumor a patient has; the accurate and early diagnosis is a very crucial step. Machine Learning (ML) algorithms have been used to develop and train the model for classification of the type of tumor. For accurate and better classification several classification algorithms in ML have been trained and tested on the dataset that was collected. Already algorithms like Naïve Bayes, Random Forest, K-Nearest Neighbor and SVM showed better accuracy for classification of tumor. When we implemented Multilayer Perceptron (MLP) algorithm it gave us the best accuracy levels among all both during training as well as testing .i.e. 97%. So, the exact classification using this model will help the doctors to diagnose the type of tumor in patients quickly and accurately

scholarly journals Classification of Mammograms using Various Feature Extraction Methods and Machine Learning

2019 ◽  
Vol 8 (2) ◽  
pp. 5401-5405
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Extraction Methods ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Multiple Features ◽  
Human Power ◽  
Hsv Histogram ◽  
Mammogram Images

Breast cancer is an alarming disease which takes millions of lives every year. In 2018, it was anticipated that 627,000 women died due to breast cancer – which is around 15% of all deaths caused due to different types of cancers among women. Currently, risk factors of breast cancer cannot be avoided, and early detection is the only way of survival. Automated detection of breast cancer with the help of image processing methods and machine learning algorithms helps in giving more accurate results and less human power. In the proposed system, multiple features are extracted using HSV histogram, LBP, GLCM, 2-D DWT. Support vector machine and LIBSVM classifiers are used for the classification of mammogram images if it’s benign or malign in nature. For classification, the INbreast dataset have been used which includes 115 cases containing 410 images. The dataset is divided into benign and malign category based upon BI-RAIDS scale. According to this partition we have 243 benign images and 100 malign images present in this dataset and a feature matrix of 595 features in total is generated for balanced and unbalanced datasets respectively and fed into SVM and LIBSVM to distinguish the data. The balanced datasets on LIBSVM gave best results with 92% accuracy, 84% sensitivity, 100% specificity and 91.30% F1 score followed by SVM which gave 75% accuracy, 73.61% sensitivity, 76.66% specificity and 75.8% F1 score.

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