Today's, of the major cancers for both females and male is lung cancer. This type of cancer is the most common cause of mortality that accounts for up to 20% of all cancers. The incidence of this cancer has noticeably increased since the beginning of the 19th century. The current study aims to investigate and present a novel method to diagnose lung cancer using Optimization Algorithm (GOA) algorithm and KNN classification. The study method includes three steps. In the first step, pre-processing of lung cancer cell data is used to remove irrelevant and duplicate features. In the second step, the Grasshopper Optimization Algorithm (GOA) method is used to select the high-dimensional feature. In the third step, the selected features are classified into three categories, namely low, medium and high using the KNN nearest neighbor classifier. To evaluate the proposed method, the UCI dataset is used. The results indicate that this method has superior performance with the accuracy of 98.65, specificity of 96.7, and sensitivity of 94.10, demonstrating the superiority of this method over others. The results show that diagnosis of lung cancer using data mining techniques provides the physician with the most detailed and accurate information in the shortest possible time.
No takes yet. Share an insight, caveat, or question.
Rahmani et al. (2019) studied this question.