Machine Learning model for detection of Breast Cancer
Anshuman, Upendra Kumar · 2021 5th International Conference on Information Systems and Computer Networks (ISCON) · 2021
Machine Learning(ML) as a sub-domain of Artificial Intelligence has been proved not less than a boon for the mankind. Machine Learning algorithms and techniques, when chosen wisely, enhances the overall output of the process by a significant margin. It has proven its worth in various sectors and finds application in almost every walk of life. Medical field is just another ground where ML plays a very vital role, especially in early detection of various disease with high accuracy. The goal of this paper is to demonstrate the implementation of Machine Learning algorithms to detect breast cancer in early stages with high accuracy to save the lives. Breast cancer is one of the most common type of cancer amongst women. Its spread among the community is a serious concern across the globe. Timely detection of the cases and due treatment are very important in this case to save the patients' life. More than 2.3 million women were diagnosed with breast cancer, out of which around 0.7 million died last year. Manual diagnosis of the disease is not very effective and usually early detection is almost impossible that leads to the death of the patients. The work presented here aims to classify the tumor diagnosed as benign or malignant, with the help of the 30 specific attributes taken from the dataset of a group of normal as well as patients that have breast cancer, by implementing multiple Machine Learning algorithms and selecting the classification model on the basis of highest attained accuracy.