Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal e...Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.展开更多
Predicting the stages of cancer accurately is crucial for effective treatment planning. In this study, we aimed to develop a model using gene expression data and XGBoost (eXtreme Gradient Boosting) that include clinic...Predicting the stages of cancer accurately is crucial for effective treatment planning. In this study, we aimed to develop a model using gene expression data and XGBoost (eXtreme Gradient Boosting) that include clinical and demographic variables to predict specific lung cancer stages in patients. By conducting the feature selection using the Wilcoxon Rank Test, we picked the most impactful genes associated with lung cancer stage prediction. Our model achieved an overall accuracy of 82% in classifying lung cancer stages according to patients’ gene expression data. These findings demonstrate the potential of gene expression analysis and machine learning techniques in improving the accuracy of lung cancer stage prediction, aiding in personalized treatment decisions.展开更多
文摘Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.
文摘Predicting the stages of cancer accurately is crucial for effective treatment planning. In this study, we aimed to develop a model using gene expression data and XGBoost (eXtreme Gradient Boosting) that include clinical and demographic variables to predict specific lung cancer stages in patients. By conducting the feature selection using the Wilcoxon Rank Test, we picked the most impactful genes associated with lung cancer stage prediction. Our model achieved an overall accuracy of 82% in classifying lung cancer stages according to patients’ gene expression data. These findings demonstrate the potential of gene expression analysis and machine learning techniques in improving the accuracy of lung cancer stage prediction, aiding in personalized treatment decisions.
