Breast Cancer
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(FIVE YEARS 109899)



Angela More

Abstract: Data analytics play vital roles in diagnosis and treatment in the health care sector. To enable practitioner decisionmaking, huge volumes of data should be processed with machine learning techniques to produce tools for prediction and classification Breast Cancer reports 1 million cases per year. We have proposed a prediction model, which is specifically designed for prediction of Breast Cancer using Machine learning algorithms Decision tree classifier, Naïve Bayes, SVM and KNearest Neighbour algorithms. The model predicts the type of tumour, the tumour can be benign (noncancerous) or malignant (cancerous) . The model uses supervised learning which is a machine learning concept where we provide dependent and independent columns to machine. It uses classification technique which predicts the type of tumour. Keywords: Cancer, Machine learning, Prediction, Data Visualization, SVM, Naïve Bayes, Classification.

2022 ◽  
Vol 76 ◽  
pp. 102094
Qiulin Wang ◽  
Mark S. Goldberg ◽  
France Labrèche ◽  
Vikki Ho

ESMO Open ◽  
2022 ◽  
Vol 7 (1) ◽  
pp. 100343
G. Nader-Marta ◽  
D. Martins-Branco ◽  
E. de Azambuja

Ahmad Alzu'bi ◽  
Maysarah Barham

<p>Breast cancer is one of the most common diseases diagnosed in women over the world. The balanced iterative reducing and clustering using hierarchies (BIRCH) has been widely used in many applications. However, clustering the patient records and selecting an optimal threshold for the hierarchical clusters still a challenging task. In addition, the existing BIRCH is sensitive to the order of data records and influenced by many numerical and functional parameters. Therefore, this paper proposes a unique BIRCH-based algorithm for breast cancer clustering. We aim at transforming the medical records using the breast screening features into sub-clusters to group the subject cases into malignant or benign clusters. The basic BIRCH clustering is firstly fed by a set of normalized features then we automate the threshold initialization to enhance the tree-based sub-clustering procedure. Additionally, we present a thorough analysis on the performance impact of tuning BIRCH with various relevant linkage functions and similarity measures. Two datasets of the standard breast cancer wisconsin (BCW) benchmarking collection are used to evaluate our algorithm. The experimental results show a clustering accuracy of 97.7% in 0.0004 seconds only, thereby confirming the efficiency of the proposed method in clustering the patient records and making timely decisions.</p>

2022 ◽  
Vol 17 ◽  
pp. 101339
Cecilia E. Thomas ◽  
Leo Dahl ◽  
Sanna Byström ◽  
Yan Chen ◽  
Mathias Uhlén ◽  

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