Case-based Reasoning with Input Text Processing to Diagnose Mood (Affective) Disorders

Abstract

Case-Based Reasoning is one of the methods used in expert systems. Calculation of similarity degree among the cases has always been an important aspect in CBR as the system will attempt to identify cases with the highest of similarity degree in a case-base to provide solutions for new problems. In this research, a CBR model with input text processing for diagnosing mood [affective] disorder is developed. It correlates with the increased tendency of mood disorder in accordance with the dynamics of the economic and political situation. Calculation of similarity degree among the cases is one of the main focuses in this research. This study proposed a new method to calculate similarity degree between cases, Modified-Tversky. The analysis performed to assess the method used in measuring case similarity reveals that the Modified-Tversky Method surpasses the other methods. In the all tests conducted, the results of case similarity measures using the Modified-Tversky method is greater than or equal to the calculations performed using the Jaccard dan Tversky methods. The test results also provide an average level of performance in processing text input is 89.3 %.