• Sandhya Devi
• Preeti Sondhi

The most frequent cause of cancer-related mortality is breast cancer. Finding cancer in its early stages is crucial. For the goal of diagnosing breast cancer data, a variety of machine learning approaches are accessible. Classification is frequently used to make judgments based on new information learned through analyzing historical data with algorithms. The number of characteristics may impact how well an algorithm performs. The K-Nearest Neighbor algorithm and the naive Bayes algorithm are two popular data mining techniques for categorization. The best method for one type of data may not be the best approach for another type of data. A smart algorithm could even perform horribly for different data types. To address this problem, the accuracy of the K-Nearest Neighbor and Naive Bayes algorithms for the classification of breast cancer will be examined in this work. The likelihood of malignant and benign breast cancer in patients with existing parameters may be predicted.

Breast Cancer, Dataset, K-NN, Naïve Bayes, classification

"Breast Cancer Detection using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 11, page no.a740-a744, November-2022, Available :http://www.jetir.org/papers/JETIR2211084.pdf

"Breast Cancer Detection using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 11, page no. ppa740-a744, November-2022, Available at : http://www.jetir.org/papers/JETIR2211084.pdf