Current Volume 8
Abstract
Breast cancer is one of the fundamental drivers for the disease spread and the leading cause of death to most women across the globe. Early diagnostics builds the odds of right treatment and endurance, however this cycle is dreary and regularly prompts a contradiction between pathologists. Although many individuals who suffer breast cancer have no family history but women who have blood relatives suffering from the same disease are at higher risk. Besides, a high risk of developing breast cancer includes aging, genes, thick breast tissues, obesity, and radiation exposure.Malignant and benign are two different types of tumors and to distinguish between these two, physicians need a reliable diagnostic procedure. The mammography method is used to detect breast cancer but radiologists exhibit significant variation in interpretation. In any case, early recognition and anticipation can altogether reduce the fatality. Henceforth, it is foremost to detect breast cancer as early as possible. In this paper, we present a prediction of breast cancer with different machine learning algorithms compare their prediction accuracy, area under the receiver operating characteristic curve (AUC) and performance parameters, wherein the model gets trained by considering the parameters such as: radius, texture, perimeter, area, smoothness, concavity, concaveness, and compactness. Here, all these parameters are taken in mean and overall values are considered. Further, these algorithms can be modified with their mathematical models to increase the prediction of breast cancer.
Keywords
Neighbouring tissues, Breast Cancer, Machine Learning, Concave Point mean, Malignant
Citations
IRE Journals:
Mudit Sharma
"Cancer: Prediction and Analysis" Iconic Research And Engineering Journals Volume 6 Issue 7 2023 Page 258-264
IEEE:
Mudit Sharma
"Cancer: Prediction and Analysis" Iconic Research And Engineering Journals, 6(7)
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