Disease diagnosis with a hybrid method SVR using NSGA-II

Zangooei, M. H ; Sharif University of Technology

1272 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.neucom.2014.01.042
  3. Abstract:
  4. Early diagnosis of any disease at a lower cost is preferable. Automatic medical diagnosis classification tools reduce financial burden on health care systems. In medical diagnosis, patterns consist of observable symptoms and the results of diagnostic tests, which have various associated costs and risks. In this paper, we have experimented and suggested an automated pattern classification method for classifying four diseases into two classes. In the literature on machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variables. There are endeavors to use regression methods to solve classification problems and vice versa. To regard a classification problem as a regression one, we propose a method based on the Support Vector Regression (SVR) classification model as one of the powerful methods in intelligent field management. We apply the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), a kind of multi-objective evolutionary algorithm, to find mapping points (MPs) for rounding a real-value to an integer one. Also, we employ the NSGA-II to find out and tune the SVR kernel parameters optimally so as to enhance the performance of our model and achieve better results. The results of the study are compared with the results of some previous studies focusing on the diagnoses of four diseases using the same UCI machine learning database. The experimental results show that the proposed method yields a superior and competitive performance in these four real-world datasets
  5. Keywords:
  6. Disease diagnosis ; Machine learning ; Multi-objective genetic algorithm ; Support vector regression ; Learning systems ; Problem solving ; Regression analysis ; Classification models ; Competitive performance ; Machine-learning database ; Multi objective evolutionary algorithms ; Non-dominated sorting genetic algorithm-II ; Support vector regression (SVR) ; Diagnosis ; Breast cancer ; Computer prediction ; Diabetes mellitus ; Evolutionary algorithm ; Hepatitis ; Intermethod comparison ; Liver disease ; Nondominated sorting genetic algorithm ; Priority journal
  7. Source: Neurocomputing ; Vol. 136 , 2014 , pp. 14-29
  8. URL: http://www.sciencedirect.com/science/article/pii/S0925231214002549