Lung Cancer Disease Prediction Using Hybrid Subtractive Clustering Method

Abstract

As of right now, lung cancer is the primary cause of cancer-related deaths worldwide for both men and women. One possible explanation for lung cancer's main cause is smoking. 86% to 96% of instances of lung cancer are thought to originate in the epithelial cells that line the larger and smaller airways (bronchi and bronchioles), despite the fact that lung cancer can grow in any section of the lung. The primary objective of this work is to identify lung cancer using the ANFIS approach based on subtractive clustering method. Using Kaggle, we have made use of the Lung Cancer dataset. The network is trained using data from 1000 lung cancer patients, ranging in age from 14 to 73. The proposed system uses 11 input attributes to assess the presence and absence of lung cancer during testing, and consistently achieves 100% accuracy for each clustering radii. © 2024 IEEE.