Version 1
: Received: 21 May 2024 / Approved: 21 May 2024 / Online: 21 May 2024 (14:42:57 CEST)
How to cite:
Asghari Ilani, M.; Moftakhar Tehran, S.; Kavei, A.; Alizadegan, H. Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach. Preprints2024, 2024051407. https://doi.org/10.20944/preprints202405.1407.v1
Asghari Ilani, M.; Moftakhar Tehran, S.; Kavei, A.; Alizadegan, H. Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach. Preprints 2024, 2024051407. https://doi.org/10.20944/preprints202405.1407.v1
Asghari Ilani, M.; Moftakhar Tehran, S.; Kavei, A.; Alizadegan, H. Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach. Preprints2024, 2024051407. https://doi.org/10.20944/preprints202405.1407.v1
APA Style
Asghari Ilani, M., Moftakhar Tehran, S., Kavei, A., & Alizadegan, H. (2024). Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach. Preprints. https://doi.org/10.20944/preprints202405.1407.v1
Chicago/Turabian Style
Asghari Ilani, M., Ashkan Kavei and Hamed Alizadegan. 2024 "Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach" Preprints. https://doi.org/10.20944/preprints202405.1407.v1
Abstract
Abstract
This study delved into the application of various machine learning (ML) models for the classification of lung cancer levels. Through meticulous monitoring of parameters such as minimum child weight and learning rate, efforts were made to mitigate overfitting while optimizing model performance. The Deep Neural Network (DNN) emerged as a standout performer, showcasing robust performance across training, validation, and testing stages. Ensemble methods like voting and bagging also demonstrated promising results. However, Support Vector Machine (SVM) models with the Sigmoid kernel faced challenges in achieving satisfactory performance. Overall, the investigation sheds light on the efficacy of different ML models in lung cancer level classification and underscores the importance of parameter tuning to address overfitting concerns.
Keywords
Deep Learning; Lung Cancer; Machine Learning; Support Vector machine
Subject
Computer Science and Mathematics, Computer Science
Copyright:
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.
