Constructing a predictive model for detection of breast cancer - Ashebo, Fantaye Ayele
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Breast cancer is the second leading cause of cancer deaths in women now a day and has become the most common cancer among women both in the developed and the developing world. Early detection is the most effective way to decrease breast cancer deaths. But early detection needs an accurate and reliable diagnosis procedure that allows doctors to differentiate benign breast tumors from malignant ones without going for surgical biopsy. Hence, the aim of this research paper is to design a predictive model for breast cancer detection using data mining techniques from breast cancer dataset that is…mehr

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Produktbeschreibung
Breast cancer is the second leading cause of cancer deaths in women now a day and has become the most common cancer among women both in the developed and the developing world. Early detection is the most effective way to decrease breast cancer deaths. But early detection needs an accurate and reliable diagnosis procedure that allows doctors to differentiate benign breast tumors from malignant ones without going for surgical biopsy. Hence, the aim of this research paper is to design a predictive model for breast cancer detection using data mining techniques from breast cancer dataset that is capable of enhancing the reliability of breast cancer disease detection. Data mining techniques may help in answering several important and critical questions by extraction of useful knowledge that support for cost-savings and decision.
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Autorenporträt
My name is Fantaye Ayele. I was born on July 23, 1990, in Hosanna, Ethiopia. I have a BSc degree in applied mathematics with minor computer science from Dilla University. And also I got my MSc from Jimma University in information science. Currently, I am working as a lecturer in the Wolaita Sodo university information systems department.