Project Description

Cancer has been characterized as a heterogeneous disease consisting of many different sub-types.The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients.The ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making.

In this project, a random forest algorithm is used to discuss the case of breast cancer case diagnosis and obtain high prediction accuracy.Random forest is one of many classification techniques, and it is an algorithm for big data classification. Random forest classification is applied here to achieve a more accurate and reliable classification performance. The accuracy in this project is 96.50%.