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- W3089283973 abstract "The leading cause of mortality in women in developing countries is breast cancer. Breast cancer is the world's secondmost common source of cancer mortality in women. In recent decades, the high incidence of breast cancer in womenhas significantly increased. Within this paper, we addressed many techniques of data mining used to diagnose breastcancer early. The diagnosis of breast cancer is a distinction between benign and malignant breast lumps. By using datamining techniques, we have approached the diagnosis of this disease. Data mining is an essential phase in exploringinformation in libraries where intelligent tools are used to identify trends. Several observational experiments have beenperformed on breast cancer using soft computation and machine learning techniques. Often say that their algorithms arequicker, simpler, or more detailed than others. This study is based on genetic programming and machine learningalgorithms designed to build a system to differentiate benign and malignant breast cancer accurately. The purpose ofthis analysis was to refine the research algorithm. We employed the genetic programming methodology to choose thebest features and parameter values of the classification machines. Data mining is an essential phase in exploringinformation in libraries where intelligent tools are used to identify trends. We are evaluating the breast cancer dataaccessible from the U.C.I. deep-learning data collection in Wisconsin. In this experiment, we compare four Wekasoftware classification techniques with genetic clustering. A comparison of the results shows that sequential minimaloptimization (S.M.O.) has higher accuracy than I.B.K. and B.F. Tree methods, i.e., 97.71%." @default.
- W3089283973 created "2020-10-01" @default.
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- W3089283973 date "2020-01-01" @default.
- W3089283973 modified "2023-09-28" @default.
- W3089283973 title "SVM &GA-CLUSTERING BASED FEATURE SELECTIONAPPROACH FOR BREAST CANCER DETECTION" @default.
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