Machine Learning Approach for Lung Cancer Detection and Classification–A Comparative Analysis
Keywords:
Decision Tree, Gray-Level Co-occurrence Matrix, Lung Cancer, Machine Learning, Naive Bayes, Principal Component Analysis.Abstract
The low percentages of cure for advanced stages of lung cancer highlight how crucial early discovery is to improving prognoses. Therefore, identifying the disease in its early stages is a potential direction in lung cancer diagnosis research. Using Principal Component Analysis (PCA) for feature extraction and Gray-Level Co-occurrence Matrix (GLCM) features for detection and classification of lung cancer, the proposed study compares several machine learning techniques. The suggested techniques are assessed using three classifiers: Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM). The goal of the study is to determine which of these classifiers is the best at correctly recognising and classifying cases of lung cancer. This study advances the understanding of machine learning strategies for improving lung cancer diagnosis and classification, perhaps leading to better patient outcomes, by carefully examining the effectiveness of each approach. Using Local Binary Patterns (LBP) to extract features led to notable improvements in all algorithms. The results show that LBP features are useful in increasing classification performance: Naive Bayes (NB) attained an accuracy of 0.851, Decision Trees (DT) to 0.912, and Support Vector Machine (SVM) to 0.961.
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