Optimization of Random Forest Algorithm for Breast Cancer Detection
Sarika Chaudhary , Yojna Arora, Neelam Yadav
Abstract
Today, cancer is a big issue and the most common disease all over the world. Cancer starts due to the abnormal growth of cells in your body. So, cancer takes place anywhere in the body. There are more than 100 types of cancers. The most common cancers are blood cancer, skin cancer, lung cancer, breast cancer etc. Nowadays, women can die because of breast cancer. There are several techniques like machine learning algorithms, big data & hadoop algorithms, and data mining algorithms to addressing breast cancer. Many techniques claim that their results were faster and more accurate. This paper presents an optimized random forest algorithm for cancer detection. Experimental results show that random forest gives the accuracy of 98.60%. All experiments are executed within anaconda package the scientific python/R development environment and spider software.
Anaconda, breast cancer, benign, malignant, machine learning, random forest algorithm, Spider, tumor.
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DOI: 10.21276/ijircst.2020.8.3.4 
