Machine Learning Algorithms Based Non Alcoholic Fatty Liver Disease Prediction
- 1. Department of Computer Science and Engineering, CMR Engineering College, Hyderabad (Telangana), India.
- 2. Assistant Professor, Department of Computer Science and Engineering, CMR Engineering College, Hyderabad (Telangana), India.
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- 1. Department of Computer Science and Engineering, CMR Engineering College, Hyderabad (Telangana), India.
Description
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.
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- Journal article: 2277-3878 (ISSN)
References
- A.Jaya Mabel Rani, S. Nishanthini, D.C.Jullie Josephine, Hridya Venugopal, S.Gracia Nissi, V. Jacintha, "Liver Disease Prediction using Semi Supervised based Machine Learning Algorithm", 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), Year: 2022.
- Lukas Brausch, Steffen Tretbar, Holger Hewener, "Identification of advanced hepatic steatosis and fibrosis using ML algorithms on high-frequency ultrasound data in patients with non-alcoholic fatty liver disease", 2021 IEEE UFFC Latin America Ultrasonics Symposium (LAUS), Year: 2021 https://doi.org/10.1109/LAUS53676.2021.9639128
- Michal Byra, Grzegorz Styczynski, Cezary Szmigielski, Piotr Kalinowski, Lukasz Michalowski, Rafal Paluszkiewicz, Bogna Ziarkiewicz-Wroblewska, Krzysztof Zieniewicz, Andrzej Nowicki, "Adversarial attacks on deep learning models for fatty Liver Disease classification by modification of ultrasound image reconstruction method", 2020 IEEE International Ultrasonics Symposium (IUS), Year: 2020
- Golmei Shaheamlung, Harshpreet Kaur, Jimmy Singla, "A Comprehensive Review of Medical Expert Systems for Diagnosis of Chronic Liver Diseases", 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Year: 2019 https://doi.org/10.1109/ICCIKE47802.2019.9004438
- R Bharath, P Rajalakshmi, "Nonalcoholic Fatty Liver Texture Characterization based on Transfer Deep Scattering Convolution Network and Ensemble Subspace KNN classifier", 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC), Year: 2019 https://doi.org/10.23919/URSIAP-RASC.2019.8738717
- Arshad I, Dutta C, Choudhury T, Thakral A, editors. "Liver disease detection due to excessive alcoholism using data mining techniques", 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE); 2018 https://doi.org/10.1109/ICACCE.2018.8441721
- Talpur N, Salleh MNM, Hussain K, editors. "An investigation of membership functions on performance of ANFIS for solving classification problems", IOP Conference Series: Materials Science and Engineering; 2017 https://doi.org/10.1088/1757-899X/226/1/012103
- Tavakkoli P, Souran DM, Tavakkoli S, Hatamian M, Mehrabian A, Balas VE, editors. "Classification of the liver disorders data using Multi-Layer Adaptive Neuro-Fuzzy Inference System (ANFIS)". 2015 6th International Conference on Computing, Communication and Networking Technologies (ICCCNT); 2015 https://doi.org/10.1109/ICCCNT.2015.7395182
- Hashem EM, Mabrouk MS. "A study of support vector machine algorithm for liver disease diagnosis", American Journal of Intelligent Systems. 2014; 4(1):9-14
- Bahramirad S, Mustapha A, Eshraghi M, editors. "Classification of liver disease diagnosis: A comparative study", 2013 Second International Conference on Informatics & Applications (ICIA); 2013 https://doi.org/10.1109/ICoIA.2013.6650227
Subjects
- ISSN: 2277-3878 (Online)
- https://portal.issn.org/resource/ISSN/2277-3878#
- Retrieval Number: 100.1/ijrte.C78760912323
- https://www.ijrte.org/portfolio-item/C78760912323/
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- Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
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