Diagnosis of Lung Cancer with Hybrid Artificial Intelligence Method

Lung cancer is a deadly disease that is difficult to diagnose and incurable. It is caused by the uncontrolled growth of cancerous cells in the lungs. Early diagnosis is vital for such an incurable disease. This study presents an artificial intelligence-based hybrid model that will detect the presence of cancer cells using various machine learning algorithms. Statistical characteristics were obtained by processing the computed tomography results from 1097 individuals, 681 cancer patients and 416 healthy. Eleven feature groups and classification models were created using kNN, SVMs, and DT techniques. Finally, a hybrid model was created that expresses the average of all machine learning algorithms. Thanks to the hybrid model, the detection of cancerous cells with %100 accuracy were achieved. Thus, as a result of detecting cancerous cells early, the risks will be eliminated, and the intervention of physicians will reduce the risk of death before the disease reaches a fatal dimension.