08 Vannucci, Marina

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Hadley Wickham

Technology Health & Medicine

Marina Vannucci Professor of Statistics Co-Director, Joint MDA-RICE Graduate Program in Biostatistics

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Ricardo Ferrari

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Abstract: We analyze the breast Cancer data available from the WBC, WDBC from UCI machine learning with the aim of developing accurate prediction models for breast cancer using data mining techniques. Data mining has, for good reason, recently attracted a lot of attention, it is a new Technology, tackling new problem, with great potential for valuable commercial and scientific discoveries. The experiments are conducted in WEKA. Several data mining classification techniques were used on the proposed data. There are many classification techniques in data mining such as Decision Tree, Rules NNge, Tree random forest, Random Tree, lazy IBK. The aim of this paper is to investigate the performance of different classification techniques. The data breast cancer data with a total 286 rows and 10 columns will be used to test and justify the different between the classification methods and algorithm. Keywords - Machine learning, data mining Weka, classification, breast cancer

Performance and Evaluation of Data Mining Techniques in Cancer Diagnosis

IOSR Journals

Breast Cancer is one of the crucial and burning diseases that has invaded women. Predicting breast cancer manually takes a lot of time and it is difficult for the physician to classification. So, detecting cancer through various automatic diagnostic techniques is very necessary. Data mining is the process of running powerful classification techniques that extract useful information from data. The uses and potentials of these techniques have found its scope in medical data. Classification techniques tend to simplify the prediction segment.

On Predicting and Analyzing Breast Cancer using Data Mining Approach

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Breast Cancer Detection using Convolution Neural Network

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According to World Health Organization (WHO), breast cancer is the top cancer in women both in the developed and the developing world. Increased life expectancy, urbanization and adoption of western lifestyles trigger the occurrence of breast cancer in the developing world. Most cancer events are diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer outcome and survival is very crucial. In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis on breast cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of the other patients are realized through seven different algorithms and the accuracies of those have been given. The data about the patients have been taken from UCI Machine Learning Repository thanks to Dr. William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process, RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.

Breast cancer diagnosis via data mining performance analysis of seven differe...

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