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: Predicting early-stage liver diseases is an essential health-related research area, as it can facilitate the early detection of diseases and prompt effective remedies. The liver diseases are classified into different types, such as liver cancer, liver tumour, fatty liver, hepatitis, cirrhosis, etc. Non-Alcoholic Fatty Liver Disease is a kind of chronic disease for which rigorous prediction is quite tricky at early stages. The prediction of fatty liver disease plays a significant role in both treating the disease and preventing its associated health consequences. This paper presents Machine Learning Algorithms based Non Alcoholic Fatty Liver Disease (NAFLD) prediction. The primary objective of this project is to identify potential factors contributing to NAFLD using Machine Learning algorithms, including the Decision Tree (DT) classifier, Support Vector Machine (SVM) classifier, Random Forest (RF) classifier, and Logistic Regression (LR). Accuracy is a parameter used for evaluating performance analysis. The findings of this paper demonstrate that the random forest model accurately predicts the presence of non-alcoholic fatty liver disease in patients.
Keywords: Liver Disease, Classification, Machine Learning, NAFLD, Electronic Health Records.
Scope of the Article: Classification