Track: Modeling and Simulation

Abstract

Although cancer is a broad term, it is an important problem that causes a high rate of death. It indicates disease that occurs when cellular changes cause cells to grow and divide uncontrollably. Cancerous cells can form tumors, damage the immune system, and cause other aberrations that prevent the body from functioning properly. In particular, lung cancer is the type of cancer with the highest death rate in the last five years. The important thing in lung cancer is early diagnosis and diagnosis to ensure the survival of patients. In this study, a model that automatically detects lung cancer has been developed in order to detect lung cancer early and take the necessary precautions at the first stage of the disease. The aim of our study is to diagnose the presence of lung cancer cells based on attributes and information from human symptoms. In our study, a dataset consisting of 26 features and 3 classes determining lung cancer as low, medium and high was used. Some preprocessing and transformations have been done to make the data more suitable for predictive analysis. The first 26 features were used as the inputs of the model. The lung cancer feature was used as the predicted output based on the input features. The values of the attributes are normalized. In the first stage, data preprocessing was carried out with various data mining techniques in order to process the data more easily and increase the performance. In the proposed model, feedforward backpropagation Artificial Neural Network model was used to detect the presence of lung cancer in the body of the person. Unlike other studies, the factors that cause lung cancer by producing our own neural network have been determined. When the results obtained are compared with the studies in the literature, it has been observed that it gives very successful results.