Prediction of Breast Cancer Disease using Machine Learning Algorithms
Dr. M. Srivenkatesh, Associate Professor, Department of Computer Science, GITAM Deemed to be University, Visakhapatnam.
Manuscript received on January 10, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on February 10, 2020. | PP: 2868-2878 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1866029420/2020©BEIESP | DOI: 10.35940/ijitee.D1866.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Background/Aim: Breast Cancer is the most often identified cancer among women and major reason for increasing mortality rate among women. The early strategies for estimating the breast cancer sicknesses helped in settling on choices about the progressions to have happened in high-chance patients which brought about the decrease of their dangers. Methods: In the proposed research, we have considered breast cancer data set from kaggle and we have done pre-processing tasks for missing values .We have no missing data values from the considered data set .The performance of the diagnosis model is obtained by using methods like classification, accuracy, sensitivity and specificity analysis. This paper proposes a prediction model to predict whether a people have a breast cancer disease or not and to provide an awareness or diagnosis on that. This is done by comparing the accuracies of applying rules to the individual results of Support Vector Machine, Random forest, Naive Bayes classifier and logistic regression on the dataset taken in a region to present an accurate model of predicting breast cancer disease. Results: The machine learning algorithms under study were able to predict breast cancer disease in patients with accuracy between 52.63% and 98.24%. Conclusions: It was shown that Random Forest has better Accuracy (98.24 %) when compared to different Machine-learning Algorithms.
Keywords: Breast Cancer, Machine Learning Algorithms, Performance Evaluators, toxins
Scope of the Article: Machine Learning