_____________________________________________________________________________________________________ *Corresponding author: Email: 1954samir@gmail.com; Journal of Engineering Research and Reports 13(2): 48-54, 2020; Article no.JERR.58329 ISSN: 2582-2926 Early Breast Cancer Prediction using Artificial Intelligence Methods Shawni Dutta 1 and Samir Kumar Bandyopadhyay 2* 1 Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India. 2 The Bhawanipur Education Society College, Kolkata, India. Authors’ contributions This work was carried out in collaboration between both authors. Author SD designed the proposed method, coding and statistical work. Author SKB initiates the work and check the manuscript written by author SD. Both authors read and approved the final manuscript. Article Information DOI: 10.9734/JERR/2020/v13i217105 Editor(s): (1) Dr. Tian- Quan Yun, South China University of Technology, China. Reviewers: (1) P. P. G. Dinesh Asanka, University of Kelaniya, Sri Lanka. (2) Ayodele Abiola Periola, Bells University of Technology, Nigeria. Complete Peer review History: http://www.sdiarticle4.com/review-history/58329 Received 02 April 2020 Accepted 08 June 2020 Published 09 June 2020 ABSTRACT In India, the death toll due to breast cancer is increasing at a rapid pace. Only early detection and diagnosis is the way of control but it is a major challenge in India due to lack of awareness and lethargy of Indian womentowards health care and regular check-up. But the major obstacle in India is expensive health care system and unavailability of proper infrastructure, especially in breast cancer treatment. This paper aims in obtaining an automated tool that will exploit patient’s health records and predict the tendency of being affected in breast cancer. Gradient Boost classifier is used as an automated tool that predicts the chance of being affected in breast cancer disease. Early detection of this disease will assist health care systems to provide counter measures in order to save patients’ life. The proposed model is evaluated against other peer classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbour (K-NN), Naïve bayes classifier, Adaboost classifier, Decision Tree (DT) classifier, and Random Forest (RF) Classifier. The proposed method achieves encouraging result with an accuracy of 97.34%, F1-Score of 0.97 Cohen-Kappa Score of 0.94 and MSE of 0.0266. The Gradient Boost algorithm attains the lowest error rate along with highest efficiency which might be the best choice of algorithm for this problem and prediction of disease. Original Research Article