The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Productivity of Information Systems in the Healthcare Industry
Information Systems Research
CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Journal of Biomedical Informatics
Clinical Decision Support Systems: Theory and Practice (Health Informatics)
Clinical Decision Support Systems: Theory and Practice (Health Informatics)
Methods to Evaluate Health information Systems in Healthcare Settings: A Literature Review
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks
Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Hi-index | 0.00 |
Common periodical health check-ups include several clinical test items with affordable cost. However, these standard tests do not directly indicate signs of most lifestyle diseases. In order to detect such diseases, a number of additional specific clinical tests are required, which increase the cost of the health check-up. This study aims to enrich our understanding of the common health check-ups and proposes a way to estimate the signs of several lifestyle diseases based on the standard tests in common examinations without performing any additional specific tests. In this manner, we enable a diagnostic process, where the physician may prefer to perform or avoid a costly test according to the estimation carried out through a set of common affordable tests. To that end, the relation between standard and specific test results is modeled with a multivariate kernel density estimate. The condition of the patient regarding a specific test is assessed following a Bayesian framework. Our results indicate that the proposed method achieves an overall estimation accuracy of 84%. In addition, an outstanding estimation accuracy is achieved for a subset of high-cost tests. Moreover, comparison with standard artificial intelligence methods suggests that our algorithm outperforms the conventional methods. Our contributions are as follows: (i) promotion of affordable health check-ups, (ii) high estimation accuracy in certain tests, (iii) generalization capability due to ease of implementation on different platforms and institutions, (iv) flexibility to apply to various tests and potential to improve early detection rates.