Early Breast Cancer Prediction using Artificial Intelligence Methods
Shawni Dutta, Samir Kumar Bandyopadhyay
Journal of Engineering Research and Reports · pp. 48–54 · Published 9 Jun 2020
10.9734/jerr/2020/v13i217105Abstract
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.
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Cited by 6
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Showing 2 of 6 known citations — external sources report more than can currently be individually listed.
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