Lung Cancer Detection Using Machine Learning Techniques.

Area of Science:

• Oncology
• Computer Science
• Artificial Intelligence

Background:

• Lung cancer is a leading cause of cancer deaths globally, with India facing a high incidence of new diagnoses.
• Early lung cancer detection is challenging due to its often asymptomatic nature.
• Computer-aided diagnostic systems offer potential solutions for accurate lung cancer identification.

Purpose of the Study:

• To develop an advanced model for lung cancer diagnosis.
• To integrate machine learning algorithms, ensemble learning, and particle swarm optimization.
• To enhance the accuracy of lung cancer detection systems.

Main Methods:

• Utilized machine learning algorithms for lung cancer diagnosis.
• Implemented ensemble learning techniques to improve diagnostic accuracy.
• Employed particle swarm optimization in conjunction with machine learning models.

Main Results:

• The proposed ensemble learning approach showed higher accuracy than traditional machine learning methods.
• Integration of advanced algorithms improved the overall performance of the diagnostic system.
• The study highlights the effectiveness of ensemble methods in lung cancer detection.

Conclusions:

• Ensemble learning techniques are highly effective for accurate lung cancer diagnosis.
• Advanced computational methods, including ensemble learning, show promise in improving early lung cancer detection.
• This research contributes to the development of more sophisticated computer-aided diagnostic tools for oncology.