abstract breast cancer is one of the major health problems of the eastern world. regardless of the survival rate improvement with progression in screening and adjuvant systemic therapies, still one – third of the patients with primary breast cancer have recurrence of micro metastasis after 10 years.  it is important to discover a reliable biomarker for detection of breast cancer. the underlying...

A challenging research problem for researchers is predicting heart problem, breast cancer, tumor, and the most daunting diseases. Current research in this area is struggling to provide accurate and better solution for the prediction of such deadly diseases. In this paper, Discriminative Rule Framing (DRF) algorithm is proposed to analyze and predict the survivability of disease in a patient. As...

background: using primary tumor gene expression has been shown to have the ability of finding metastasis-driving gene markers for the prediction of breast cancer recurrence (bcr). however, there are some difficulties associated with the analysis of microarray data which led to poor predictive power and inconsistency of the previously introduced gene signatures. methods: in this study a hybrid m...

background: breast cancer is the most common type of cancer in women demanding time and accurate diagnosis to take remedial measures to treat. objective: comparing the diagnostic capability of the computer regulation thermography (crt), as a novel and safe dignostic procedure, with common clinical examination and imaging methods including sonography and mammography for diagnosing breast cancer ...

<p>Breast cancer is one of the significant deaths causing diseases women around globe. Therefore, high accuracy in prediction models vital to improving patients’ treatment quality and survivability rate. In this work, we presented a new method namely improved balancing particle swarm optimization (IBPSO) algorithm predict stage breast using unbalanced surveillance epidemiology end result ...

background: numerous studies used microarray gene expression data to extract metastasis-driving gene signatures for the prediction of breast cancer relapse. however, the accuracy and generality of the previously introduced biomarkers are not acceptable for reliable usage in independent datasets. this inadequacy is attributed to ignoring gene interactions by simple feature selection methods, due...

Feature selection is an essential preprocessing step for removing redundant or irrelevant features from multidimensional data to improve predictive performance. Currently, medical clinical datasets are increasingly large and not every feature helps in the necessary predictions. So, techniques used determine relevant set that can performance of a learning algorithm. This study presents analysis ...