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This data set considers information of time to cosmetic deterioration of the breast for women with Stage 1 breast cancer who have undergone a lumpectomy, for two treatments, these being radiation, and radiation coupled with chemotherapy. There is interest in the cosmetic impact of the treatments because both are considered very effective in preventing recurrence of this early stage cancer. The data come from a retrospective study of 46 patients who received radiation only and 48 who received radiation plus chemotherapy.

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Obesity and its impact on health is a multifaceted phenomenon encompassing many factors, including demographics, environment, lifestyle, and psychosocial functioning. A systems science approach, investigating these many influences, is needed to capture the complexity and multidimensionality of obesity prevention to improve health. Leveraging baseline data from a unique clinical cohort comprising postmenopausal overweight or obese breast cancer survivors participating in a weight-loss trial, we applied Bayesian networks, a machine learning approach, to infer interrelationships between lifestyle factors e.

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Data classification is one of the most common tasks in the field of statistical analysis and countless methods have been developed for this purpose over time. A common approach is to develop a model based on known historical data, i. Applications of data classifications permeate virtually all fields of study, including social sciences, engineering, biology, etc.

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Skip to search form Skip to main content. Construction of a Bayesian network for mammographic diagnosis of breast cancer. Kahn and Louise M.

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Skip to content. Skip to navigation. Background: Gene expression analyses indicate that breast cancer is a heterogeneous disease with at least 5 immunohistologic subtypes.

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You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iim:iimawp See general information about how to correct material in RePEc.

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Skip to search form Skip to main content. Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience. Burnside and Daniel L.

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