General Letters in Mathematics

Volume 15 - Issue 2 (1) | PP: 31 - 41 Language : English
DOI : https://doi.org/10.31559/glm2025.15.2.1
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A Comparative Study of Machine Learning Algorithms for Lung Cancer Diagnosis: Evaluating Performance Using ROC Curve, Accuracy, Recall, and F1 Score Metrics

Fakhereldeen E.E Musa ,
Elnazeer Mohamed Elnoor
Received Date Revised Date Accepted Date Publication Date
9/3/2025 15/4/2025 7/5/2025 26/6/2025
Abstract
This study aims to compare the performance of four popular machine learning algorithms in the field of Classification for lung cancer diagnosis, namely: Decision Tree, Random Forest, Support Vector Machines (SVM), and Logistic Regression: Decision Tree, Random Forest, Support Vector Machines (SVM), Support Vector Machines (SVM), and Logistic Regression. The importance of this study comes from the need to improve the accuracy of lung cancer diagnosis, which is one of the most common and dangerous cancers, using machine learning techniques that can deal with complex and multidimensional data. A dataset containing information about patients, such as age, smoking habits, chemical exposure, and other influencing factors, was used. The performance of the algorithms was evaluated using multiple metrics, including Accuracy, Precision, Recall, and F1 Score. The results showed that the Random Forest algorithm achieved the highest accuracy and best performance in dealing with complex data, while Logistic Regression showed a good ability to interpret influential factors and provide analytical insights. Based on these results, the study recommends the use of the Random Forest algorithm in lung cancer diagnosis applications that require high accuracy, considering the role of logistic regression in analyzing influencing factors. It also recommends the importance of exploring additional improvements to the algorithms to increase their effectiveness in dealing with larger and more complex datasets.


How To Cite This Article
Musa , F. E. & Elnoor , E. M. (2025). A Comparative Study of Machine Learning Algorithms for Lung Cancer Diagnosis: Evaluating Performance Using ROC Curve, Accuracy, Recall, and F1 Score Metrics. General Letters in Mathematics, 15 (2), 31-41, 10.31559/glm2025.15.2.1

Copyright © 2025, This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.