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Lung Cancer Prediction from Text Datasets Using Machine Learning.

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This study introduces a machine learning model for early lung cancer detection, achieving 98.8% accuracy. This computational intelligence approach offers sustainable, cost-effective healthcare solutions for smart cities.

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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.