Version 1
: Received: 25 May 2024 / Approved: 5 June 2024 / Online: 6 June 2024 (02:29:01 CEST)
How to cite:
Asghari Ilani, M.; Kavei, A.; Moftakhar Tehran, S. Advancing Lung Cancer Classification through Machine Learning: A Comprehensive Comparative Analysis of Model Performance. Preprints2024, 2024060300. https://doi.org/10.20944/preprints202406.0300.v1
Asghari Ilani, M.; Kavei, A.; Moftakhar Tehran, S. Advancing Lung Cancer Classification through Machine Learning: A Comprehensive Comparative Analysis of Model Performance. Preprints 2024, 2024060300. https://doi.org/10.20944/preprints202406.0300.v1
Asghari Ilani, M.; Kavei, A.; Moftakhar Tehran, S. Advancing Lung Cancer Classification through Machine Learning: A Comprehensive Comparative Analysis of Model Performance. Preprints2024, 2024060300. https://doi.org/10.20944/preprints202406.0300.v1
APA Style
Asghari Ilani, M., Kavei, A., & Moftakhar Tehran, S. (2024). Advancing Lung Cancer Classification through Machine Learning: A Comprehensive Comparative Analysis of Model Performance. Preprints. https://doi.org/10.20944/preprints202406.0300.v1
Chicago/Turabian Style
Asghari Ilani, M., Ashkan Kavei and Saba Moftakhar Tehran. 2024 "Advancing Lung Cancer Classification through Machine Learning: A Comprehensive Comparative Analysis of Model Performance" Preprints. https://doi.org/10.20944/preprints202406.0300.v1
Abstract
Lung cancer remains a pervasive global health challenge, necessitating precise and efficient classification methods to inform optimal treatment strategies. In this study, we investigate the potential of machine learning algorithms in developing a computer-aided diagnostic tool for early and accurate lung cancer classification, leveraging essential protein biomarkers as predictive features. Our investigation reveals the superiority of Deep Neural Networks (DNNs) in lung cancer classification, achieving an impressive accuracy of 96.91%. This highlights the transformative potential of DNNs in facilitating real-world clinical applications, offering clinicians a powerful tool for accurate diagnosis and prognosis. Additionally, we analyze the performance of Ensemble Methods, including Voting (91.75%) and Bagging (93.81%), as viable alternatives to DNNs. These ensemble techniques demonstrate robust performance, further underlining their utility in lung cancer classification tasks. Furthermore, our study underscores the critical role of hyperparameter tuning, particularly in adjusting parameters such as min child weights and learning rates, in mitigating overfitting and enhancing the generalizability of diverse machine learning models. For instance, Support Vector Machines (SVM) exhibit varying accuracies ranging from 74.23% to 95.88% across different hyperparameter configurations, emphasizing the importance of hyperparameter optimization in maximizing model performance. In summary, our comprehensive comparative analysis sheds light on the efficacy of different machine learning approaches in lung cancer classification. By leveraging advanced algorithms and optimizing model parameters, researchers and clinicians can harness the power of machine learning to advance early detection and personalized treatment strategies for lung cancer patients.
Public Health and Healthcare, Health Policy and Services
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.
