• NAMRATA KATAKI
• Sanjana Dutta Choudhury
• Hrishikesh Gogoi
• Swaraj Bora
• Pratyosh Pawan Bora

Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The objective of this paper is to describe a predictive model built using Machine Learning algorithms for early detection of breast cancer in order to improve the prognosis and chances of survival through timely clinical treatment to patients. Most influential machine learning algorithms have been used for comparison in terms of accuracy, sensitivity, specificity and precision. Data mining approaches, for instance, applied to medical science topics rise rapidly due to their high performance in predicting outcomes, reducing costs of medicine, promoting patients’ health, improving healthcare value and quality and in making real time decision to save people’s lives. Therefore, the correct diagnosis of breast cancer and classification of patients into malignant and benign groups is the center of a lot of research. The early diagnosis of breast cancer can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumors can prevent patients undergoing unnecessary treatments.

Breast Cancer, Machine Learning, Classification, Malignant, Benign

"Implementation of Machine Learning in Early Detection of Breast Cancer ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.c261-c267, May-2022, Available :http://www.jetir.org/papers/JETIR2205240.pdf

"Implementation of Machine Learning in Early Detection of Breast Cancer ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppc261-c267, May-2022, Available at : http://www.jetir.org/papers/JETIR2205240.pdf