Proceedings ArticleDOI
Breast Cancer Prediction Applying Different Classification Algorithm with Comparative Analysis using WEKA
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TLDR
The capability of the classification of Naïve Bayes, Random Forest, Logistic Regression, Multilayer Perceptron, K-nearest neighbors in evaluating the Breast Cancer Disease dataset culled from UCI machine learning repository, was observed to predict the existence of Breast cancer.Abstract:
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 (zero to fourth) and may include surgery, radiation, or chemotherapy. 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 capability of the classification of Naive Bayes, Random Forest, Logistic Regression, Multilayer Perceptron, K-nearest neighbors in evaluating the Breast Cancer Disease dataset culled from UCI machine learning repository, was observed to predict the existence of Breast cancer. Data set has been explored in terms of Kappa Statistics, TP rate, FP Rate and precision.read more
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Journal ArticleDOI
Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis
TL;DR: The comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer is presented to find out the most appropriate method that will support the large dataset with good accuracy of prediction.
Journal ArticleDOI
Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning
Aditya Khamparia,Subrato Bharati,Prajoy Podder,Deepak Gupta,Ashish Khanna,Thai Kim Phung,Dang N. H. Thanh +6 more
TL;DR: The proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks and can be considered as an effective tool for radiologists to decrease the false negative and false positive rates of mammograms.
Posted Content
Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review.
TL;DR: A systematic review of the literature on artificial neural network (ANN) based models for the diagnosis of breast cancer via mammography found that the best performance was achieved by residual neural network-50 and ResNet-101 models of CNN algorithm.
Journal ArticleDOI
Expert cancer model using supervised algorithms with a LASSO selection approach
TL;DR: The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression, decision tree, decisionTree, random forest, and K-nearest neighbor based on a 10-fold cross-validation approach.
References
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BookDOI
An introduction to statistical learning
TL;DR: An introduction to statistical learning provides an accessible overview of the essential toolset for making sense of the vast and complex data sets that have emerged in science, industry, and other sectors in the past twenty years.
Book
An Introduction to Statistical Learning: with Applications in R
TL;DR: This book presents some of the most important modeling and prediction techniques, along with relevant applications, that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
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Logistic Regression: A Primer
TL;DR: The Logic of Logistic Regression Interpreting Logistic regression Coefficients Estimation and Model Fit Probit Analysis as mentioned in this paper is a well-known approach to model fit probability analysis.
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