A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment.

Clicks: 298
ID: 67626
2019
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Abstract
Cancer is caused by the un-controlled division of abnormal cells in a body part. Various cancers exist in this world and one amongst them is breast cancer. Breast cancer (BC) threatens the lives of people and today, it is the secondary prime cause of death in women. Numerous research directions concentrated on the prediction of BC. The prevailing prediction model is time-consuming and have less accuracy. To trounce those drawbacks, this paper proposed a BC prediction system (BCPS) utilizing Optimized Artificial Neural Network (OANN). Primarily, the unprocessed BC data are regarded as the input. The big data (BD) storage comprises some repeated information. Secondarily, such repeated data are eliminated by utilizing Hadoop MapReduce. In the subsequent stage, the data are preprocessed utilizing replacing of missing attributes (RMA) and normalization techniques. Subsequently, the features are generally chosen by utilizing Modified Dragonfly algorithm (MDF). Then, the selected features are inputted for classification. Here, it classifies the features utilizing OANN. Optimization is done by employing the Gray Wolf Optimization (GWO) algorithm. Experiential outcomes are contrasted with prevailing IWDT (Improved Weighted-Decision Tree) in respect of precision, recall, accuracy, and ROC.
Reference Key
supriya2019ahealth Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Supriya, M;Deepa, A J;
Journal health care management science
Year 2019
DOI
10.1007/s10729-019-09498-w
URL
Keywords
big data gray wolf optimization (gwo) improved weighted decision tree (iwdt) modified dragonfly algorithm (mdf) optimized artificial neural network (oann)

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