Machine Learning Algorithms Based Non Alcoholic Fatty Liver Disease Prediction

ba60f6118992d8a5a2-342b 20230921071802870 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 09 30 2023 12 3 Machine Learning Algorithms Based Non Alcoholic Fatty Liver Disease Prediction Department of Computer Science and Engineering, CMR Engineering College, Hyderabad (Telangana), India. Bindu Bhargavi Munukuntla https://orcid.org/0009-0004-7643-0370 Mrutyunjaya S. Yalawar Assistant Professor, Department of Computer Science and Engineering, CMR Engineering College, Hyderabad (Telangana), India. The early stage liver diseases prediction is an important health related research and using this kind of research easily can predict the diseases and take the remedies. The liver diseases are classified into different types such as liver cancer, liver tumor, fatty liver, hepatitis, cirrhosis etc. Non-Alcoholic Fatty Liver Disease is a kind of chronic disease which rigorous prediction is quite difficult at early stages. The prediction of fatty liver plays significant role in treating the disease and also constraining the next health consequences. This paper presents Machine Learning Algorithms based Non Alcoholic Fatty Liver Disease (NAFLD) prediction. The main objective of this project is to identify the potential factors causing NAFLD by using Machine Learning algorithms like Decision Tree (DT) classifier, Support Vector Machine (SVM) classifier, Random Forest (RF) classifier, Logistic regression (LR). Accuracy is used parameter for performance analysis evaluation. The findings of this paper show that random forest model accurately predicts a non-alcoholic fatty liver disease patient. 09 30 2023 43 46 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true International Journal of Recent Technology and Engineering (IJRTE) No, I did not receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Not relevant. All authors having equal contribution for this article. 10.35940/ijrte.C7876.0912323 https://www.ijrte.org/portfolio-item/C78760912323/