1. Machine Learning Algorithms for Binary Classification of Liver Disease
- Author
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Galyna Kondratenko, Anton Sokoliuk, Anatoly Khomchenko, Igor Atamanyuk, Yuriy P. Kondratenko, and Ievgen Sidenko
- Subjects
Artificial neural network ,Computer science ,business.industry ,Decision tree ,Machine learning ,computer.software_genre ,medicine.disease ,Random forest ,Support vector machine ,Liver disease ,Naive Bayes classifier ,Binary classification ,medicine ,Artificial intelligence ,business ,Algorithm ,computer ,Contaminated food - Abstract
The number of patients with liver diseases has been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles, and drugs. Early diagnosis of liver problems will increase patients’ survival rates. Liver disease can be diagnosed by analyzing the levels of enzymes in the blood. Creating automatic classification tools may reduce the burden on doctors. To achieve this numerous classification algorithm (Decision Tree, Random Forest, SVM, Neural Net, Naive Bayes, and others) from different machine learning libraries (Scikit-learn, ML.Net, Keras) are tested against existing liver patients’ dataset, considering appropriate for each algorithm preliminary data processing. These algorithms evaluated based on three criteria: accuracy, sensitivity, specificity.
- Published
- 2020
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