Machine Learning Algorithms Based Non Alcoholic Fatty Liver Disease Prediction
Bindu Bhargavi Munukuntla1, Mrutyunjaya S Yalawar2
1Bindu Bhargavi Munukuntla, Department of Computer Science and Engineering, CMR Engineering College, Hyderabad (Telangana), India.
2Mrutyunjaya S. Yalawar, Assistant Professor, Department of Computer Science and Engineering, CMR Engineering College, Hyderabad (Telangana), India.
Manuscript received on 20 July 2023 | Revised Manuscript received on 28 July 2023 | Manuscript Accepted on 15 September 2023 | Manuscript published on 30 September 2023 | PP: 43-46 | Volume-12 Issue-3, September 2023 | Retrieval Number: 100.1/ijrte.C78760912323 | DOI: 10.35940/ijrte.C7876.0912323
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Liver Disease, Classification, Machine Learning, NAFLD, Electronic Health Records.
Scope of the Article: Classification