Early Prediction of Lung Cancer Using Gaussian Naive Bayes Classification Algorithm
Keywords:
lung cancer detection, early prediction, Gaussian Naive Bayes, machine learning, accuracy, E-Health Care SystemAbstract
The early prediction of lung cancer is of utmost importance for improving patient survival rates. However, accurately diagnosing lung cancer poses a significant challenge for radiologists. In recent times, the field of medicine has witnessed numerous innovations through the adoption of machine learning (ML) techniques, particularly in the context of E-Health Care Systems. These techniques have proven valuable in the early detection of lung cancer. This study proposes the implementation of the Gaussian Naive Bayes (GNB) classification algorithm to detect lung cancer at its nascent stages. The researchers assess the performance of the GNB algorithm by employing a lung cancer dataset obtained from the University of California, Irvine (UCI). To gauge the effectiveness of GNB, its results are compared against other popular ML techniques such as K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and the J48 algorithm (a variant of the C4.5 decision tree algorithm). Notably, the performance analysis reveals that the GNB algorithm achieves an impressive 98% accuracy in predicting lung cancer. This signifies the promising potential of GNB for accurate and early-stage detection of lung cancer. By leveraging the distinctive characteristics of the Gaussian Naive Bayes algorithm and utilizing the lung cancer dataset, the researchers successfully demonstrate its efficacy in achieving a high level of accuracy. This research contributes to the on-going efforts in improving lung cancer diagnosis and emphasizes the significance of early prediction in enhancing patient outcomes.
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