An Inference Engine for Estimating Outside States of Clinical Test Items

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.