Machine learning-based lung cancer diagnosis

Abstract

Cancer is one of the leading health problems, occurring in various organs and tissues of the body, and its incidence is increasing worldwide. Lung cancer is one of the deadliest types of cancer. Due to its worldwide prevalence, increasing number of cases, and deadly consequences, early detection of lung cancer, as with all other cancers, greatly increases the chances of survival. As with all other diseases, the diagnosis of cancer is only possible after the appearance of various symptoms and an examination by specialists. Known symptoms of lung cancer are shortness of breath, coughing, wheezing, jaundice in the fingers, chest pain, and difficulty swallowing. The diagnosis is made by an expert on site based on these symptoms and additional tests. The aim of this study is to detect the disease at an earlier stage based on the symptoms present, to assess more cases with less time and cost, and to achieve results in new situations that are as successful or even faster than those of human experts by deriving them from existing data using different algorithms. The aim is to develop an automated model that can detect early-stage lung cancer based on machine learning methods. The developed model includes nine different machine learning algorithms (NB, LR, DT, RF, GB, and SVM). The success of the classification algorithms used was evaluated using the metrics of accuracy, sensitivity, and precision calculated using the parameters of the confusion matrix. The results obtained show that the proposed model can detect cancer with a maximum accuracy of 91%.

Keywords

• Lung cancer
• Early diagnosis
• Machine learning
• Classification

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