Summary of Exploring Machine Learning Models For Lung Cancer Level Classification: a Comparative Ml Approach, by Mohsen Asghari Ilani et al.
Exploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach
by Mohsen Asghari Ilani, Saba Moftakhar Tehran, Ashkan Kavei, Hamed Alizadegan
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates machine learning (ML) models for diagnosing lung cancer levels, aiming to enhance diagnostic accuracy and prognosis. The study evaluates various ML algorithms by adjusting parameters and conducting rigorous testing. Techniques like minimum child weight and learning rate monitoring were employed to combat overfitting and optimize performance. Results show that Deep Neural Network (DNN) models performed robustly across all phases. Ensemble methods, including voting and bagging, demonstrated promise in improving predictive accuracy and robustness. In contrast, Support Vector Machine (SVM) models with the Sigmoid kernel struggled, highlighting the need for further refinement. The study underscores the importance of parameter tuning to optimize model performance and improve diagnostic accuracy in oncological care. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at using machine learning to better diagnose lung cancer. Scientists tried different computer programs to see which ones worked best. They made adjustments to get the best results and tested them thoroughly. Deep Neural Networks were very good at this task, while other methods like voting and bagging also showed promise. However, some other algorithms struggled. The study shows that making these computer programs work well is important for improving lung cancer diagnosis. |
Keywords
» Artificial intelligence » Bagging » Machine learning » Neural network » Overfitting » Sigmoid » Support vector machine
