Earticle

초록

영어

It is evident from various researches that disease diagnosis using machine learning methods has been increasing rapidly. In this research work, feature selection based Least Square Twin Support Vector Machine (LSTSVM), which is a machine learning method, is used for diagnosis of heart diseases. In this approach F-score is used to calculate the weight of each feature and then features are selected according to their weight. The higher weight is assigned to the feature having high F-score. Grid search approach is also utilized to select the best value of classifier's parameters in order to enhance its performance. The heart-statlog disease dataset is used in this study, which is taken from the UCI repository. The performance of proposed model with different feature sets has been evaluated for different training-test datasets. The results indicate that LSTSVM model with 11 features has achieved highest accuracy. The results are very promising as compared to the other approaches proposed earlier.

목차

Abstract
 1. Introduction
 2. Least Square Twin Support Vector Machine
 3. Methodology and Experiments
  3.1 Heart statlog-Dataset
  3.2 Feature Selection
  3.3 Setting Model parameters
  3.4 LSTSVM model with Grid Search and Feature Selection
  3.5 Measures for Performance Evaluation
 4. Results and Discussion
 5. Conclusion
 References

키워드

저자

• Divya Tomar [ Indian Institute of Information and Technology, Allahabad, India ]
• Sonali Agarwal [ Indian Institute of Information and Technology, Allahabad, India ]

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