The week's pick
Random Articles
Reseach Article
Machine Learning Algorithms for Detecting Liver Disease: The Review
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 5 |
| Year of Publication: 2023 |
| Authors: V.S. Mane, S.T. Bhosale |
10.5120/ijca2023922703
|
V.S. Mane, S.T. Bhosale . Machine Learning Algorithms for Detecting Liver Disease: The Review. International Journal of Computer Applications. 185, 5 ( Apr 2023), 17-23. DOI=10.5120/ijca2023922703
Abstract
Liver disease is the cause of death and effect of disease is very high on human. many researchers are studied on these predicting detect liver disease with help of medical report blood test, biopsy test, platelets, common liver function test like Alanine Transaminase, Aspartate Transaminase, Alkaline phosphatase, Albumin and total proteins, Bilirubin, Gamma-glutamyl transferase, L-lactate dehydrogenase, Prothrombin time. Upcoming days liver disease increase day by day as per various research paper study. Machine learning algorithms are very useful for biomedical domain or medical field. Mainly they are used for detecting disease. In this review paper studied and overview of some different research paper. And these different research papers give a description of machine learning algorithm like logistic regression, random forest, decision tree, Support Vector Machine, K-nearest, Naïve Bayes that are used for predicting detecting different liver disease.
References
- A.K.M Sazzadur Rahman, F. M. Javed Mehedi Shamrat, Zarrin Tasnim, Joy Roy, Syed Akhter Hossain, “A Comparative Study on Liver Disease Prediction Using Supervised Machine Learning Algorithms”International journal of scientific & technology Research volume 8, 2019.
- Ibrahim Mahmood Ibrahim, Adnan Mohsin Abdulazee, “The Role of Machine Learning Algorithms for Diagnosing Diseases”, JASTT, Vol. 02, No. 01, pp. 10 – 19 (2021)
- Dr. S. Vijayarani, Mr.S. Dhayanand,“Liver Disease Prediction using SVM and Naïve Bayes Algorithms”, International Journal of Science, Engineering and Technology Research (IJSETR) Volume 4, Issue 4, April 2015
- Nazmun Nahar1 and Ferdous Ara, “Liver Disease Prediction by Using Different Decision Tree Techniques”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.8, No.2,2018
- M. Banu Priya, P. Laura Juliet, P.R. Tamilselvi “Performance Analysis of Liver Disease Prediction Using Machine Learning Algorithms”, International Research Journal of Engineering and Technology (IRJET),2018
- Chieh-Chen Wu, Wen-Chun Yeh, Wen-Ding Hsu, Md. Mohaimenul Islam, Phung Anh (Alex) Nguyen , Tahmina Nasrin Poly, Yao-Chin Wang, Hsuan-Chia Yang, Yu-Chuan (Jack) Li ,“Prediction of fatty liver disease using machine learning algorithms”,2019
- Elias Dritsas, Maria trigka, “Supervised Machine Learning Models for Liver Disease Risk Prediction”, 2023
- Neha Tanwar1 and Khandakar Faridar Rahman, “Machine Learning in liver disease diagnosis: Current progress and future opportunities”, IOP Publishing,2021
- Hands-On Machine Learning with Scikit-Learn and TensorFlow, by Aurélien Géron Released March 2017, Publisher(s): O'Reilly Media,
- Introduction to Machine Learning with Python: A Guide for Data Scientists - Andreas C, Andreas C. Müller, Sarah Guido "O'Reilly Media, Inc.", 2016
- Lee, J. H., Joo, I., Kang, T. W., Paik, Y. H., Sinn, D. H., Ha, S. Y., ... & Bang, W. C. (2020). Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network. European radiology, 30, 1264-1273.
- Sontakke, S., Lohokare, J., & Dani, R. (2017, February). Diagnosis of liver diseases using machine learning. In 2017 International Conference on Emerging Trends & Innovation in ICT (ICEI) (pp. 129-133). IEEE.
- Wu, C. C., Yeh, W. C., Hsu, W. D., Islam, M. M., Nguyen, P. A. A., Poly, T. N., ... & Li, Y. C. J. (2019). Prediction of fatty liver disease using machine learning algorithms. Computer methods and programs in biomedicine, 170, 23-29.
- Ma, H., Xu, C. F., Shen, Z., Yu, C. H., & Li, Y. M. (2018). Application of machine learning techniques for clinical predictive modeling: a cross-sectional study on nonalcoholic fatty liver disease in China. BioMed research international, 2018.
Index Terms
Keywords