Lung Cancer Prediction using Machine learning Techniques
Keywords:
Decision Tree, KNN, Logistic Regression, Machine Learning, , Naïve Bayes, SVMAbstract
A thorough analysis and assessment of several research projects using machine learning algorithms to create prediction models for the diagnosis of lung cancer. The main aim of this research is to identify innovative insights into artificial intelligence techniques that can enhance cancer prognosis, early detection, and overall health outcomes. The study utilized a dataset comprising 309 individuals with and without lung cancer, selected based on family history, and incorporated multiple attributes such as gender, age, smoking, allergies, among others. The most important features of the lung cancer dataset were selected using logistic regression, and the findings were validated using k-fold 10 cross-validation. The proposed model demonstrated superior performance compared to other machine learning techniques, achieving an accuracy of 92.2% and an AUC of 93.6%. The development of a reliable method for lung cancer early detection is the main goal of this study.
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Copyright (c) 2023 Saif Al Rumhi, Raza Hasan; Saqib Hussain, Jitendra Pandey
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