Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis
TLDR
A performance comparison between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB) and k Nearest Neighbors (k-NN) on the Wisconsin Breast Cancer datasets is conducted and Experimental results show that SVM gives the highest accuracy with lowest error rate.About:
This article is published in Procedia Computer Science.The article was published on 2016-01-01 and is currently open access. It has received 501 citations till now. The article focuses on the topics: Support vector machine & Decision tree.read more
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COVID-19 Future Forecasting Using Supervised Machine Learning Models
Furqan Rustam,Aijaz Ahmad Reshi,Arif Mehmood,Saleem Ullah,Byung-Won On,Waqar Aslam,Gyu Sang Choi +6 more
TL;DR: The results prove that the ES performs best among all the used models followed by LR and LASSO which performs well in forecasting the new confirmed cases, death rate as well as recovery rate, while SVM performs poorly in all the prediction scenarios given the available dataset.
Posted Content
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
TL;DR: A comprehensive review of federated learning systems can be found in this paper, where the authors provide a thorough categorization of the existing systems according to six different aspects, including data distribution, machine learning model, privacy mechanism, communication architecture, scale of federation and motivation of federation.
Journal ArticleDOI
Deep Learning Based Analysis of Histopathological Images of Breast Cancer.
TL;DR: The experimental results demonstrate that using the proposed autoencoder network results in better clustering results than those based on features extracted only by Inception_ResNet_V2 network, which is the best deep learning architecture so far for diagnosing breast cancers by analyzing histopathological images.
Journal ArticleDOI
Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images
TL;DR: Results show a high sensitivity in the identification of COVID-19, around 100%, and with a high degree of specificity, which indicates that it can be used as a screening test.
Proceedings ArticleDOI
LightGBM: An Effective miRNA Classification Method in Breast Cancer Patients
Dehua Wang,Yang Zhang,Yi Zhao +2 more
TL;DR: As a powerful tool, LightGBM can be used to identify and classify miRNA target in breast cancer, and hsa-mir-139 was found as an important target for the breast cancer classification.
References
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Cancer statistics, 2016
TL;DR: Overall cancer incidence trends are stable in women, but declining by 3.1% per year in men, much of which is because of recent rapid declines in prostate cancer diagnoses, and brain cancer has surpassed leukemia as the leading cause of cancer death among children and adolescents.
Journal ArticleDOI
Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Journal ArticleDOI
What is a support vector machine
TL;DR: Support vector machines are becoming popular in a wide variety of biological applications, but how do they work and what are their most promising applications in the life sciences?
Proceedings Article
Transductive Inference for Text Classification using Support Vector Machines
TL;DR: An analysis of why Transductive Support Vector Machines are well suited for text classi cation is presented, and an algorithm for training TSVMs, handling 10,000 examples and more is proposed.
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