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Nowadays, lung cancer has become one of the important topics for researchers in medical technology. Lung cancer is one of the deadliest diseases in the world and is a subject of concern because no actual treatment has been found for this disease yet.
Computer-aided diagnosis (CAD) is one of the widely used imaging techniques for detecting and grading lung cancer. This thesis presents a proposed system for classifying lung cancer after its detection with the help of machine learning algorithms, where several steps are used in the form of stages which include the stage of data acquisition, data pre-processing, and classification. The theater.
The dataset used is obtained from the archive (data scientist), which contains 1,000 samples and 25 features. The first proposed model is based on the Support Vector Machine (SVM) classifier), and the second proposed model uses an artificial neural network (ANN) classifier and compares the accuracy and time taken for each model. Each model implements two types of preprocessing algorithms (standardization and normalization).
The results showed that the first proposed model using (SVM) with normalization had an average accuracy of 98.21%, while with standardization the accuracy was 100.00%. The second proposed model using (ANN) with normalization and standardization with an accuracy of 100.0%.