文章基本信息

标题：Evaluation of Data Mining Categorization Algorithms on Aspirates Nucleus Features for Breast Cancer Prediction and Detection
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作者：Gajendra Sharma
期刊名称：International Journal of Education and Management Engineering(IJEME)
印刷版ISSN：2305-3623
电子版ISSN：2305-8463
出版年度：2020
卷号：10
期号：2
页码：28-37
DOI：10.5815/ijeme.2020.02.04
出版社：MECS Publisher
摘要：With the development of technology the use of Computer Aided Diagnosis has become a key for breast cancer diagnosis. It is important to increase the accuracy and effective of such systems. The concept of data mining can be applied on the data gathered through such systems for prediction and prevention of breast cancer. In this research, we have conducted the comparison between seven classification algorithms with the help of WEKA (The Waikato Environment for Knowledge Analysis) tool on the 569 instances (10 nucleus attributes) of data with two classes Malignant(M) and Benign (B) of breast cancer aspirate cells. Furthermore the influence of each attribute on prediction was evaluated. The accuracy of these algorithms was above 91% with the highest value of 94.02% for random forest and the predictive power of conclave points was highest whereas lowest was of Fractal Dimension.
关键词：Data Mining; Breast Cancer; Classification Techniques; Prediction; Diagnosis;WEKA