Volume 9 • Issue 1 • PP: 30–36 • 2025
Lung Cancer Prediction from Smoking Cause by Machine Learning Classification Models
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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This article was handled by an independent editor. The author serving on the Editorial Board had no involvement in the peer-review process or the final editorial decision.
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The incidence of lung cancer varies in males and females, which occurs due to the abnormal and uncontrolled growth of cells in the lungs. It has a greater predilection in males as compared to females. Smoking is the most important risk factor for lung cancer. It causes serious breathing issues and also affects other organs. It increases the mortality rate both in young adults as well as in the older age group. Therefore, there is improvement in medical technologies to facilitate specialized diagnosis and treatment, but the mortality has not been controlled to a satisfactory extent. It is important to take preventive measures and precautions at the initial stages. Machine learning brings various advancements to the medical sector due to which various diseases can be detected at an early stage. In this paper, we presented different machine learning classifier techniques used for the classification of the present lung cancer data in the UCI machine learning repository as benign and malignant. The dataset is divided into cancerous and non-cancerous by converting the input data into binary form and using the classifier technique in the Weka tool. This specifically includes classifiers used: Logistic Regression, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Decision Trees, and Naïve Bayes. In addition, we study the effect of data preprocessing methods on our prediction accuracy, such as data normalization and feature selection. The study seeks to help develop various reliable resources for lung cancer identification, which are critical for diagnosing and treating patients in a timely manner and improving their outcomes.
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