• Pratibha Awasthi
• Dr. Shaila Chugh

Abstract: Background: One of the illnesses that kill a lot of people each year worldwide is breast cancer. It can be difficult to identify and treat this kind of illness early on in order to lower the death toll. These days, a variety of machine learning and data mining approaches are employed in medical diagnostics, which has demonstrated its effectiveness in making predictions about chronic diseases like cancer that might potentially save the lives of those who suffer from them. Finding the prediction accuracy of classification algorithms such as Support Vector Machine, J48, Naïve Bayes, and Random Forest and suggesting the optimal approach is the main goal of this work. Objective: This study aims to evaluate the efficiency and efficacy of the categorization algorithms in terms of prediction accuracy. Methodology: Using the open-source WEKA tool, this paper applies a 10-fold cross-validation technique to the Wisconsin Diagnostic Breast Cancer dataset, analyzing the prediction accuracy of various classification algorithms, including Support Vector Machine, J48, Naïve Bayes, and Random Forest. Findings(Results): According to the study's findings, Support Vector Machine has the best prediction accuracy, at 97-89%, and the lowest error rate, at 0.14%. In Conclusion: This paper provides a clear view over the performance of the classification algo- rithms in terms of their predicting ability which provides a helping hand to the medical practition- ers to diagnose the chronic disease like breast cancer effectively. Keywords: : Diagnosis, classification algorithms, Breast cancer, Machine Learning, Data mining, SVM, J48.

"Utilizing Cutting-Edge Machine Learning Methods for Breast Cancer Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.a274-a279, January-2026, Available :http://www.jetir.org/papers/JETIR2601032.pdf

"Utilizing Cutting-Edge Machine Learning Methods for Breast Cancer Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppa274-a279, January-2026, Available at : http://www.jetir.org/papers/JETIR2601032.pdf