International Journal on Science and Technology

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Prediction Of Lung Cancer Using Supervised Machine Learning

Author(s) Mr. Gourab Mukhopadhaya, Mr. Suman Sett, Mr. Sumit Basu, Prof. Susmit Chakraborty, Mr. Swarnendu Shil, Mr. Arijit Sen, Ms. Priti Munyan
Country India
Abstract Lung cancer remains one of the leading causes of cancer-related mortality worldwide, necessitating early detection for improved survival rates. This study focuses on analysing various detection techniques for lung cancer, integrating supervised machine learning and logistic regression-based approaches. Lung cancer cannot be avoided but it can be controlled. It is better to predict lung cancer at stages I & II, so that the chances of getting come round is high. This model offers a hand-held grief solution by enabling early and cost-effective detection of lung cancer. This study encloses us a model that can predict the risk of lung cancer affection by working out on the possibilities. Here all insights are invented using Supervised Machine learning logistic regression to be more exact. It focuses on creating models that can be trained from extensive datasets. The key features of this approach are gender, age, smoking, yellow fingers, anxiety, peer pressure, chronic disease, fatigue, allergy, wheezing, alcohol consumption, coughing, shortness of breath, swallowing difficulty and chest pain. The whole system is trained in Jupyter notebook. It is evaluated across diverse datasets and it demonstrates robustness, interpretability, and potentiality for clinical integration.
Keywords Cancer prediction, Machine learning model, Logistic regression, Confusion matrix, NumPy, Pandas, Jupyter notebook
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 3, July-September 2025
Published On 2025-07-18
DOI https://doi.org/10.71097/IJSAT.v16.i3.7105
Short DOI https://doi.org/g9t2wp
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