• sachin yadav
• Rahul yadav
• gaurav singh
• Vivek Kumar Sharma

The lung cancer is considered to be the deadliest type of a disease. At the moment we cannot diagnose it in time without the involvement of the medical staff. Despite the fact that we are still far away from ultimate understanding of cancer’s mechanisms and a solid cure, early diagnosis significantly improve the odds of successful treatment. By taking up the new technology including machine learning, image processing and many more, there would be a promising way for accurate diagnosis and prediction of cancer. In the latest experiments, scientists concentrated on designing an accurate method that could be valuable in image processing and machine learning to classify and predict lung cancer. The data gathering started with gathering images which was accomplished with 83 CT scans data from 70 different patients as the dataset. Before the segmentation, the images undergo preprocessing, such as noise reduction and enhancement through geometric mean filtering, thus, upgrading image quality. Additionally, the Khan is divided into healthy and affected areas using the cluster analysis method. This approach enabled targeting areas in which cancer had occurred, hence giving a good platform on which the identification and forecast of cancer would be carried out. Additionally, the ANN, KNN, and RF machine learning algorithms were utilized for the classification process. A comparison showed that the ANN model was the most consistent in their ability to predict lung cancer cases.

Data preprocessing, Data Visualization Feature selection ,Model building, Model evaluation

"LUNG CANCER PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.c6-c13, May-2024, Available :http://www.jetir.org/papers/JETIR2405202.pdf

"LUNG CANCER PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppc6-c13, May-2024, Available at : http://www.jetir.org/papers/JETIR2405202.pdf