Lung Cancer Prediction from Text Datasets Using Machine Learning

Area of Science:

• Computational intelligence
• Machine learning
• Healthcare technology

Background:

• Lung cancer is a leading cause of cancer-related mortality.
• Early detection is crucial for effective lung cancer treatment.
• Sustainable and cost-effective healthcare solutions are needed.

Purpose of the Study:

• To develop a sustainable prototype model for lung cancer treatment using computational intelligence.
• To optimize lung cancer detection through machine learning.
• To enhance healthcare delivery in smart cities.

Main Methods:

• Implemented a machine learning model based on Support Vector Machines (SVMs).
• Utilized Python for model implementation and classification of lung cancer patients based on symptoms.
• Evaluated model effectiveness using various criteria and cancer datasets from the University of California, Irvine library.

Main Results:

• The proposed SVM model achieved a 98.8% accuracy rate.
• Demonstrated a cost-effective and efficient method for real-time lung cancer treatment.
• The model outperforms existing SVM and SMOTE methods.

Conclusions:

• Smart cities can provide improved healthcare services with this model.
• Enables real-time, cost-effective lung cancer treatment accessible anytime, anywhere.
• The developed model offers a sustainable approach to lung cancer management.