Machine Learning Algorithms for Lung Cancer Detection: Application of Different Machine Learning Algorithms for Lung Cancer Detection.

EasyChair Preprint no. 12795

Abstract

Machine learning algorithms have emerged as powerful tools for lung cancer detection, offering the potential to improve accuracy and efficiency in diagnosing this life-

threatening disease. This topic focuses on the application and evaluation of various

machine learning algorithms for the purpose of lung cancer detection. The performance

and effectiveness of algorithms, including support vector machines (SVM), random

forests, deep learning models, and ensemble methods, are explored to achieve accurate

classification of lung cancer cases. Support vector machines (SVM) have been widely employed in lung cancer detection due

to their ability to handle high-dimensional data and effectively separate different classes. SVM algorithms leverage a hyperplane to maximize the margin between positive and

negative instances, resulting in robust classification. The performance of SVM models in

terms of accuracy, sensitivity, specificity, and area under the receiver operating

characteristic curve (AUC-ROC) is evaluated to assess their suitability for lung cancer

detection. Random forests, another popular machine learning algorithm, utilize an ensemble of

decision trees to classify lung cancer cases. By aggregating the predictions of multiple

decision trees, random forests can reduce overfitting and improve generalization

capabilities. The performance metrics of random forest models, including accuracy, precision, recall, and F1 score, are examined to gauge their effectiveness in accurately classifying lung cancer cases

Keyphrases: cancer treatment, Lung Cancer, Machine learning algorithm for lung cancer

@Booklet{EasyChair:12795,
  author = {Emmanuel Idowu and Lucas Doris and Axel Egon},
  title = {Machine Learning Algorithms for Lung Cancer Detection: Application of Different Machine Learning Algorithms for Lung Cancer Detection.},
  howpublished = {EasyChair Preprint no. 12795},

  year = {EasyChair, 2024}}