Authors : Sumit Mhaske; Mandar Dakhorkar; Vanshika Khiyani; Rudrani Patil; Ganesh Shelke

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/4jukp4xx

Scribd : https://tinyurl.com/yf9z3uxa

DOI : https://doi.org/10.38124/ijisrt/25apr1335

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Abstract : Lung cancer is still among the top cancers to cause cancer death in humans around the world. It has a lot to do with lifestyle and smoking- individual factors that contribute to lung cancer development. This research study seeks to analyze the viability of the machines through algorithms for the likely risk prediction of lung cancer through survey data- that is, symptoms, behavioral traits, and demographic data. The dataset consists of information such as smoking habits along with anxiety levels, fatigue, and other symptoms employed. Various machine learning models were trained and evaluated on Logistic Regression, Decision Tree, Random Forest, Naive Bayes, and Support Vector Machines (SVM) algorithms. Among those, Random Forest proved to be the best predictor giving about 96.7% accuracy and strong precision and recall values, indicating its effectiveness in identifying high-risk subjects. This research indicates that machine learning can be applied to healthcare for early diagnosis and screening.

Keywords : Lung Cancer; Machine Learning; Prediction Model; Survey Data; Random Forest; Health Informatics; Risk Assessment.

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