http://www.iaeme.com/IJARET/index.asp 871 editor@iaeme.com International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 10, October 2020, pp. 871-881, Article ID: IJARET_11_10_087 Available online at http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=10 ISSN Print: 0976-6480 and ISSN Online: 0976-6499 DOI: 10.34218/IJARET.11.10.2020.087 © IAEME Publication Scopus Indexed EXPEDITION OF MACHINE LEARNING TECHNIQUES TO SCRUTINIZE STAGING OF HEPATOCELLULAR CARCINOMA Vyshali J Gogi VTU Research Scholar, Department of MCA RV College of Engineering®, Bengaluru, Karnataka, India Dr. Vijayalakshmi M.N Associate Professor, Department of MCA RV College of Engineering®, Bengaluru, Karnataka, India Dr. Roshan. B.A. Rao Surgical Oncologist Malnad Hospital, and Institute of Oncology, Shimoga, Karnataka, India ABSTRACT Machine learning is a speculation where in the computer learns beyond the need for programming it in a precise function. Machine learning enables system to learn from data and the model can comply with the new data. Healthcare is a sophisticated field which generates tremendous data every day. Hepatocellular Carcinoma (HCC) being a liver malignancy often leads to highest mortality when not treated on time. HCC is diagnosed in stages, early to severe which is dealt by following staging system. The current work is on staging of HCC based on the imaging reports of the patients. Machine learning regression techniques is applied on the dataset. Regression techniques are applied and compared to obtain better accuracy. Keywords: Clinical, Data Pre-processing, Hepatocellular Carcinoma (HCC), Liver Function Test (LFT), Machine Learning, Pathologic, Regression, TNM (tumor (T), node (N), Metastases (M)), Staging. Cite this Article: Vyshali J Gogi, Dr. Vijayalakshmi M.N and Dr. Roshan. B.A. Rao, Expedition of Machine Learning Techniques to Scrutinize Staging of Hepatocellular Carcinoma, International Journal of Advanced Research in Engineering and Technology, 11(10), 2020, pp. 871-881 http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=10