Comparative Performance Analysis of Lung Cancer Detection using Naïve Bayes, Support Vector Machine, K-Nearest Neighbor and Decision Tree
Keywords:
Comparative, Decision tree, K-nearest neighbor, Lung cancer, Naïve Bayes, Support vector machineAbstract
This study aims to determine the best classification technique for lung cancer detection. Four different machine learning algorithms are implemented, which are Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor Classifier (KNN), and Decision Tree (DT). The classification was carried out on 140 CT Scan data from the Lung Image Database Consortium image collection (LIDC-IDRI) dataset. Furthermore, the preposition started with a variety of filtering methods, The segmentation used was Otsu thresholding, which was textured with extraction using 11 features. The best results were obtained using DT, Low pass filter, and GLCM segmentation angle of 450 with performance results of 99.00% accuracy, 100.00% sensitivity, and 98.04% specificity for training data, as well as 96.25% accuracy, 95.12% sensitivity and 97.44 % specificity for test data.
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