Feature Selection and Random Forest Classification for Breast Cancer Disease
Shubham Raj, Swati Singh, Avinash Kumar, Sobhangi Sarkar, Chittaranjan Pradhan · 2021
Medical applications such as detection of the type of cancerous cells has a great use of machine learning in it. As the modern day is progressing the researches in the world of Machine Learning and Artificial Intelligence are expanding speedily. Machine Learning gets better if we keep discovering manifold applications on a wider basis. Predictive models are the foundation of machine learning. If the accuracy is perfect the model will be more efficient and better will be the solution to a specific obstacle. Breast cancer disease is reason for a heavy count of deaths every year. It is the most common type of cancer and the main cause of women deaths worldwide. Breast Cancer is one of the most typical type of disease which occurs in women. It's a fact that cancer can be cured if it's found at an earlier stage but a large number of people are examined with cancer very late. We have discussed the techniques and mathematics behind some algorithms used in machine learning and how that can help in detection of Breast Cancer. Our research aims at determining the features which are mostly responsible for breast cancer. We have used different methods of feature selection for detection of Breast Cancer and further, we have applied Random Forest Classification on our model. We have got a good result and further it can be preferred for the detection of Breast Cancer.