• JAYA MEHRA

In this study, a breast cancer (BC) detection system using computer-aided design (CAD) is presented. Using the BC Wisconsin data-set from the UCI repository, which uses attribute clump thickness as an evaluation class, random forest, logistic regression, decision trees, SVM , KNN , and conventional neural networks (CNNs) classifiers are used in BC detection. These machine learning (ML) based algorithms are trained to predict BCs (malignant or benign). Every experiment is carried out on the JUPYTER platform in a simulation environment. The research objective is divided into three categories. The accuracy and F-measure of these machine learning algorithms are used to assess their efficacy; random forest fared better than the other classifiers, achieving 93% accuracy and 96% F-measure.

Breast Cancer , Machine Learning , Cancer Detection , UCI , CAD System

"A Methodological Research On Prediction of Breast Cancer Based On Machine Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.c45-c53, April-2024, Available :http://www.jetir.org/papers/JETIR2404208.pdf

"A Methodological Research On Prediction of Breast Cancer Based On Machine Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppc45-c53, April-2024, Available at : http://www.jetir.org/papers/JETIR2404208.pdf