Machine Learning Techniques and Extreme Learning Machine for Early Breast Cancer Prediction
Chhaya Gupta1, Nasib Singh Gill2

1Chhaya Gupta*, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India.
2Prof. Nasib Singh Gill, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on February 10, 2020. | PP: 163-167 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1411029420/2020©BEIESP | DOI: 10.35940/ijitee.D1411.029420
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Abstract: Breast Cancer is one of the most deadly disease and most of the women are infected by this vital disease in many parts of the world. Medical tests conducted in hospitals for determining the disease are very much expensive as well as time consuming. The problem can be resolved by diagnosing the problem in early spam of time and by providing results with more accuracy. In this paper, different machine learning and neural network algorithm have been studied and compared to predict cancer in early stages so that life can be saved. The dataset available publically for Breast Cancer has been used. Different algorithms compared include Support Vector Machine Classification (SVM), K-Nearest Neighbor Classification (KNN), Decision tree Classification (DT), Random Forest Classification (RF) and Extreme Learning Machine (ELM).All are compared on the basis of Accuracy and processing time are considered as the parameters for comparing analysis. The results reveal that extreme learning machine comes to be the better algorithm. 
Keywords: Decision tree classification (DT), Extreme Learning Machine (ELM),KNN classification, Random Forest (RF) classification, Support Vector Machine (SVM) classification.
Scope of the Article: Learning Machine