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payavard 2019, 13(1): 81-90 |
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Shahraki M R, Mesgar M. Evaluation of Data Mining Algorithms for Detection of Liver Disease. payavard 2019; 13 (1) :81-90
URL: http://payavard.tums.ac.ir/article-1-6702-en.html
URL: http://payavard.tums.ac.ir/article-1-6702-en.html
1- Assistant Professor, Industrial Engineering Department, Faculty of Engineering Shahid Nikbakht, Sistan and Balochestan University, Zahedan, Iran
2- Master of Sciences Student in Industrial Engineering, Faculty of Engineering Shahid Nikbakht, Sistan and Baluchestan University, Zahedan, Iran ,mhb@pjs.usb.ac.ir
2- Master of Sciences Student in Industrial Engineering, Faculty of Engineering Shahid Nikbakht, Sistan and Baluchestan University, Zahedan, Iran ,
Abstract: (3482 Views)
Background and Aim: The liver, as one of the largest internal organs in the body, is responsible for many vital functions including purifying and purifying blood, regulating the body's hormones, preserving glucose, and the body. Therefore, disruptions in the functioning of these problems will sometimes be irreparable. Early prediction of these diseases will help their early and effective treatment. Regarding the importance of liver diseases and increasing number of patients, the present study, using data mining algorithms, aimed to predict liver disease.
Materials and Methods: This descriptive study was performed using 721 data from liver patient in zahedan. In this study, after preprocessing data, data mining techniques such as SVM: Support Vector Machine, CHAID, Exhaustive CHAID and boosting C5.0, data were analyzed using IBM SPSS Modeler 18 data mining software.
Result: The validity obtained for boosting C5.0 94/09, for Exhaustive CHAID algorithm 88/71, for SVM 87/09, for CHAID algorithm 85/47 prediction of liver disease. the boosting C5.0 algorithm showed a better performance of this algorithm among other algorithms.
Conclusion: According to the rules created by boosting C5.0 algorithm, for a new sample, one can predict the likelihood of a person for developing liver disease with high precision.
Materials and Methods: This descriptive study was performed using 721 data from liver patient in zahedan. In this study, after preprocessing data, data mining techniques such as SVM: Support Vector Machine, CHAID, Exhaustive CHAID and boosting C5.0, data were analyzed using IBM SPSS Modeler 18 data mining software.
Result: The validity obtained for boosting C5.0 94/09, for Exhaustive CHAID algorithm 88/71, for SVM 87/09, for CHAID algorithm 85/47 prediction of liver disease. the boosting C5.0 algorithm showed a better performance of this algorithm among other algorithms.
Conclusion: According to the rules created by boosting C5.0 algorithm, for a new sample, one can predict the likelihood of a person for developing liver disease with high precision.
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