Citation :

Bhavya Mittal, Pari Sharma, Princy Sharma, Priya Dwivedi, Ravindra Chauhan, "Lung Cancer Risk Prediction using Random Forest and Logistic Regression," International Journal of Computer Trends and Technology (IJCTT), vol. 74, no. 4, pp. 45-51, 2026. Crossref, https://doi.org/10.14445/22312803/IJCTT-V74I4P104

Abstract

Lung cancer remains one of the leading causes of death worldwide, making early detection important for improving patient outcomes. This study presents a machine learning-based method to predict lung cancer risk by using clinical symptoms along with environmental air quality factors. The system was developed using a dataset of 3000 balanced records containing patient symptoms, Air Quality Index (AQI), and PM2.5 levels. Feature engineering was used to create combined indicators such as smoke anxiety, breath-cough patterns, and pollution exposure. Random Forest and Logistic Regression models were compared, giving accuracies of 56% and 52%, respectively. Although the accuracy is moderate, the results show that environmental factors can contribute to early risk assessment. Among the two models, Random Forest performed better because it captured nonlinear relationships more effectively. This work provides a simple approach that may support preliminary lung cancer risk screening.

Keywords

Lung Cancer Diagnosis, Air Quality Index (AQI), Machine Learning, Feature Engineering, Random Forest.

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