• Ambika L G
• Dr. T N Anitha
• Dr. Jayasudha K

Every year, the number of people who die from breast cancer rises. It is the most frequent type of cancer in women and the top cause of death worldwide. For a healthy person, advancements in the identification and prediction of malignant illnesses are critical. To update patient treatment prospects and survival standards, high accuracy in predicting the growth of malignancies is required. ML approaches have been exhibited to significantly affect the most common way of screening bosom disease early finding and forecast. The Wisconsin Breast Cancer Diagnostic Dataset utilized six Machine learning calculations: Support Vector Machine (SVM), Random Forest, Logistic Regression, Naive Bayes (NB), Decision Tree (C4.5) and KNearest Neighbor (KNN). We performed a performance evaluation and comparison between these different classifiers after receiving the results. In the wake of obtain the outcomes, we played out an exhibition assessment and an examination between these various classifiers. Support vector machines have been found to outperform all other classifiers and achieve the highest accuracy of (97.3%).

Breast cancer; Prediction; Diagnostic; SVM; NB; Logistic regression; C4.5; k-NN; Classification; Effectiveness; Accuracy; Precision.

"Selecting the Best Machine Learning Methods for Breast Cancer Risk Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.d608-d613, June-2022, Available :http://www.jetir.org/papers/JETIR2206376.pdf

"Selecting the Best Machine Learning Methods for Breast Cancer Risk Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppd608-d613, June-2022, Available at : http://www.jetir.org/papers/JETIR2206376.pdf