Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis

Abstract

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.

Predicting Lung Cancer with K-Nearest Neighbors (KNN): A Computational Approach

“…The versatility of KNN in handling lung cancer prediction is evident across these studies, with its application ranging from analyzing patient characteristics and symptoms to evaluating medical images. Despite the competition from other algorithms like SVM, Randon Forest, and Decision Tree; KNN remains a valuable tool in the arsenal against lung cancer due to its ability to provide satisfactory prediction results with relatively high accuracy and lower false detection rates compared to some other methods (6)(7)(8)(9)(10) . This collective research underscores the importance of continuing to refine and test KNN within the context of lung cancer prediction to harness its full potential in aiding early diagnosis and treatment.…”

“…Current research lacks a comprehensive evaluation of ensemble machine learning techniques against traditional models, particularly in the context of lung cancer prediction, highlighting a gap in comparative analysis and optimization of ensemble classifiers. There is a scarcity of studies focusing on the predictive performance of novel algorithms like XGBoost in comparison to traditional methods such as KNN for lung cancer prediction, suggesting an area for further exploration and validation (7) . The effectiveness of deep learning methods, specifically CNN, SVM, DT and RF, in lung cancer prediction using medical images has been demonstrated, yet there is limited research on comparing these approaches with other machine learning algorithms to establish a benchmark for accuracy and efficiency.…”