• Usha Naik
• Suresha D
• H N Prakash

Among the leading causes of fatalities from cancer is still lung cancer deaths globally, with its initial phases often presenting without noticeable symptoms, making timely diagnosis challenging. Traditional detection methods are time-consuming, require expert interpretation, and are prone to diagnostic delays. Here, we propose a machine learning-based approach in order to use structured patient data to identify lung cancer early, including symptoms, lifestyle factors, and demographic information. The model leverages renowned for its resilience, the Random Forest algorithm and high classification performance in medical diagnostics. By automating the the recognition of high-risk individuals, this method aims to enhance early diagnosis, reduce manual effort, and support clinical decision-making. Experimental findings show that the model attains a precision of 97%, demonstrating its potential as a reliable, cost-effective, and scalable tool for carcinoma of the lung prediction. The application of such intelligent systems can greatly enhance patient outcomes in the medical field and contribute to timely medical intervention.

LUNG CANCER DETECTION USING MACHINE LEARNING-A DATA DRIVEN APPROACH

"LUNG CANCER DETECTION USING MACHINE LEARNING-A DATA DRIVEN APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.961-964, July-2025, Available :http://www.jetir.org/papers/JETIRGX06178.pdf

"LUNG CANCER DETECTION USING MACHINE LEARNING-A DATA DRIVEN APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp961-964, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06178.pdf