International Journal of Engineering Technology and Management Sciences Website: ijetms.in Issue: 2 Volume No.7 March - April – 2023 DOI:10.46647/ijetms.2023.v07i02.017 ISSN: 2581-4621 @2023, IJETMS | Impact Factor Value: 5.672 | Page 133 Early Detection Of Breast Cancer Using Logistic Regression Method Saheb karan 1 , Abhik Roy Chowdhury 2 , Amit Pal 3 , Susmita Das 4 , Mrs. Sulekha Das 5 , Avijit Kumar Chaudhuri 6 1 UG -Computer Science and Engineering, Techno Engineering College Banipur 2 UG - Computer Science and Engineering, Techno Engineering College Banipur 3 UG - Computer Science and Engineering, Techno Engineering College Banipur 4 UG - Computer Science and Engineering, Techno Engineering College Banipur 5 Assistant Professor, Department of CSE, TEC Banipur. 6 Assistant Professor, Department of CSE, TEC Banipur. Corresponding Author Orcid ID :- 0000-0002-0067-5994  ABSTRACT :- Breast cancer is the most frequently occurring cancer disease in women. It is reported almost 14% of cancers in Indian women are breast cancer. It becomes very crucial to predict breast cancer earlier to minimize the deaths. This research article helps to predict breast cancer earlier and reduce the immature deaths of women in India. In this paper, the authors have used the Logistic Regression method to classify the disease. The authors simulate the results using logistic regression with 10-fold cross-validations and with a different train-test split of the dataset. The 10-fold cross validations display its potential with almost 94% performance in the research paper. With all features and 90-10 , 80-20,50-50, 66-34 splits, and 10-fold cross-validations the authors achieve 96% accuracy. we have used different accuracy measures like accuracy, sensitivity, specificity, and kappa statistics to get the novelty of the model. In this study, the authors use the Wisconsin (Diagnostic) Data Set for Breast Cancer, Created by Dr. William H. Wolberg, General Surgery Dept., University of Wisconsin, Clinical Science Centre, Madison, WI 53792 wolberg@eagle.surgery.wisc.edu available at the UCI ML Repository website. Keywords—Machine Learning, Logistic Regression, Breast Cancer.  INTRODUCTION :- Breast cancer is considered a multifactorial disease and the most common cancer in women worldwide [ 1 , 2 ] with approximately 30% of all female cancers [ 3 , 4 ] (i.e. 1.5 million women are diagnosed with breast cancer each year, and 500,000 women die from this disease in the world). Over the past 30 years, this disease has increased, while the death rate has decreased. However, the reduction in mortality due to mammography screening is estimated at 20% and improvement in cancer treatment is estimated at 60% [ 5 , 6 ]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9175124/#:~:text=The%20proposed%20machine% 2Dlearning%20approaches,interventions%20at%20the%20right%20time. This paper was constructed on Machine learning (ML) algorithms to examine the dataset of 569 cases with breast cancer and thereby explain the results. ML is a subset of artificial intelligence (AI) that is utilize to classify data based on models which have been developed and for predictive analytics, in particular breast cancer. It provides tools via which huge amount of data can be automatically analyzed. In the case of the present study, we utilized ML algorithms and collected a scientific dataset of breast cancer cases from surgery wisc edu . (wolberg@eagle.surgery.wisc.edu )and clarify these data based on various features. Ten (10) real-valued features including: [1] radius (mean of distances from center to points on the perimeter), [2]