Comparative Analysis of the Performance of KNN, SVM, and Neural Network Algorithms in Lung Cancer Classification
DOI:
https://doi.org/10.25181/rt.v4i2.5058Keywords:
lung cancer, data classification, comparative analysis, k-nearest neighbor, support vector machine, neural networkAbstract
Lung cancer is one of the leading causes of cancer-related deaths worldwide, making accurate classification methods essential to support early diagnosis. Although various machine learning algorithms have been applied to lung cancer classification, their reported performance remains inconsistent across different studies. Therefore, a comparative analysis using the same dataset is needed to provide a more objective evaluation of algorithm performance. This study aims to compare the performance of the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Neural Network (NN) algorithms for lung cancer classification. The dataset was obtained from Kaggle and consists of 309 instances with 16 attributes. The research process included data preprocessing, classification, and model evaluation using Orange Data Mining. The performance of each algorithm was evaluated using a confusion matrix based on accuracy, precision, and recall metrics. The experimental results indicate that the Neural Network algorithm achieved the best performance, with an accuracy of 92.3%, a precision of 95.4%, and a recall of 95.9%, followed by Support Vector Machine with an accuracy of 89.9% and K-Nearest Neighbor with an accuracy of 89.1%. These findings demonstrate that the Neural Network algorithm is more effective in learning the underlying patterns of the dataset than the other two algorithms. The results of this study are expected to serve as a reference for selecting appropriate classification algorithms in the development of machine learning-based lung cancer diagnosis support systems.Downloads
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