Decision-Making Model Based on Ensemble Method in Auxiliary Medical System for Non-Small Cell Lung Cancer
Huanze Chen, Wangping Xiong, Jia Wu, Qinghe Zhuang, Genghua Yu
Abstract
In many countries, lung cancer is the chief cancer type, and the overall survival rate in 5 years remains at a low rate of 16.8%. Among lung cancer patients, the proportion of patients with non-small cell lung cancer (NSCLC) can reach 85%. Especially in developing countries, it has become a great challenge for doctors to diagnose efficiently because of the complex diagnostic process of NSCLC, a large number of patients, and limited medical resources. Based on the above situation, our main objective in this study is to construct an auxiliary Artificial Intelligence medical system for doctors to diagnose the NSCLC patients efficiently. In this research, we select the combination of Support Vector Machine (SVM) and Artificial Neural Network (ANN) to complete the classification and training tasks of the medical system. In this study, we trained and tested our decision model based on the data information of 12,186 patients in three hospitals in China. The results of this experiment show that the accuracy of our system has reached 88% when the amount of data reaches 4000, which is close to that of doctors.