Clinical Decision Support Systems: From the Perspective of Small and Imbalanced Data Set


Par O. E. , Akcapinar Sezer E., Sever H.

17th International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH), Athens, Greece, 5 - 07 July 2019, vol.262, pp.344-347 identifier identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 262
  • Doi Number: 10.3233/shti190089
  • City: Athens
  • Country: Greece
  • Page Numbers: pp.344-347

Abstract

Clinical decision support systems are data analysis software that supports health professionals' decision - making the process to reach their ultimate outcome, taking into account patient information. However, the need for decision support systems cannot be denied because of most activities in the field of health care within the decision-making process. Decision support systems used for diagnosis are designed based on disease due to the complexity of diseases, symptoms, and disease-symptoms relationships. In the design and implementation of clinical decision support systems, mathematical modeling, pattern recognition and statistical analysis techniques of large databases and data mining techniques such as classification are also widely used. Classification of data is difficult in case of the small and / or imbalanced data set and this problem directly affects the classification performance. Small and/or imbalance dataset has become a major problem in data mining because classification algorithms are developed based on the assumption that the data sets are balanced and large enough. Most of the algorithms ignore or misclassify examples of the minority class, focus on the majority class. Most health data are small and imbalanced by nature. Learning from imbalanced and small data sets is an important and unsettled problem. Within the scope of the study, the publicly accessible data set, hepatitis was oversampled by distance-based data generation methods. The oversampled data sets were classified by using four different machine learning algorithms. Considering the classification scores of four different machine learning algorithms (Artificial Neural Networks, Support Vector Machines, Naive Bayes and Decision Tree), optimal synthetic data generation rate is recommended.