Earticle

초록

영어

Various types of stress and irregular eating habits, as well as inhalation of alcohol and ongoing toxic gas, ingestion of contaminated food, excessive consumption of pickled food and drug intake, enables liver disease patients to grow up year by year. To this end, variety of data mining algorithms can help medical doctors in diagnostics of patients at the hospital. This paper treats an evaluation of the analyzed results of classification algorithms selected for better prediction based on the characteristics of data from the data set with liver disease. We investigated and analyzed the classification algorithms such as Naïve Bayes, Decision Tree, Multi-Layer Perceptron and k-NN used in a previous study, which developed our data set, and additionally Random forest, Logistic which proposed by us. Those algorithms were compared in several kinds of evaluating criteria like precision, recall, sensitivity, specificity, and so on. Through the experiments, we could know that in view of precision, Naïve Bayes is preferable than others, but in other criteria such as Recall and Sensitivity, Logistic and Random Forest took precedence over other algorithms in the performance of prediction test as considering the algorithmic characteristics to liver patient data set.

목차

Abstract
 1. Introduction
 2. Materials & Methods
  2.1. Data and Descriptions
  2.2. Classification Algorithms
  2.3. Evaluation Criteria
  2.4. Deep Understanding for Algorithms and Criteria
 3. Experiments
  3.1. Previous Study Result
  3.2. Our Prediction Test
 4. Results
 5. Discussion
 6. Conclusion
 References

키워드

저자

• Hoon Jin [ Dept. of Computer Engineering, Sungkyunkwan University, South Korea ]
• Seoungcheon Kim [ Dept. of Information Network, Hansung University, South Korea ]
• Jinhong Kim [ Dept. of Computer Engineering, Hansung University, South Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605