COMPARATIVE ANALYSIS OF CIRRHOSIS PATIENTS’ MEDICAL DATA BY USING SUPERVISED MACHINE LEARNING ALGORITHMS

dc.contributor.authorMyrzagulova E.N.
dc.date.accessioned2023-10-31T11:18:15Z
dc.date.available2023-10-31T11:18:15Z
dc.date.issued2018
dc.description.abstractAbstract. The number of patients who have liver disease (cirrhosis) have been continuously increasing in the last decades because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. By this time, computer intelligence allows physicians to significantly improve the accuracy of diagnoses, create super-effective drugs and significantly alleviate the course of various diseases of patients. Automatic classification tools may reduce burden on doctors. This paper evaluates the selected classification algorithms for the classification of some liver patient datasets. There were several algorithms that were used in this research work such as Logistic Regression, Naïve Bayes classifier, k-nearest neighbors, Neural Network algorithm and Random Forest. These algorithms are evaluated based on three criteria: Accuracy, Precision, and Recall. Nowadays computer-aided diagnosis plays an important role in medicine and has no difference from the diagnosis of professional doctors. The aim of the research project is to work out an optimized algorithm for the detection Cirrhosis of the liver by using supervised machine learning algorithms, which can help to reduce costs and human resources. So the main purpose is to show that less complex algorithms can be used in order to establish a diagnosis of different cancer diseases and implement them.
dc.identifier.citationE.N. Myrzagulova / COMPARATIVE ANALYSIS OF CIRRHOSIS PATIENTS’ MEDICAL DATA BY USING SUPERVISED MACHINE LEARNING ALGORITHMS / СДУ хабаршысы - 2018
dc.identifier.issn2415-8135
dc.identifier.urihttps://repository.sdu.edu.kz/handle/123456789/676
dc.language.isoen
dc.publisherСДУ хабаршысы - 2018
dc.subjectCirrhosis of the liver, Supervised Learning, Gaussian Naive Bayes, Correlation analysis, Performance Analysis, Neural networks, Random Forest, Logistic regression.
dc.subjectСДУ хабаршысы - 2018
dc.subject№2
dc.titleCOMPARATIVE ANALYSIS OF CIRRHOSIS PATIENTS’ MEDICAL DATA BY USING SUPERVISED MACHINE LEARNING ALGORITHMS
dc.typeArticle
dspace.entity.type

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