Case Retrieval Algorithm Using Similarity Measure and Adaptive Fractional Brain Storm Optimization for Health Informaticians

Abstract

Managing and utilizing health information is recently a challenging task for health informaticians to provide the highest quality healthcare delivery. Here, storage, retrieval, and interpretation of healthcare information are important phases in health informatics. Accordingly, the retrieval of similar cases based on the current patient data can help doctors to identify the similar kind of patients and their methods of treatments. By taking into consideration this as an objective of the work, a hybrid model is developed for retrieval of similar cases through the use of case-based reasoning. Here, a new measure called parametric-enabled similarity measure is proposed and a new optimization algorithm called adaptive fractional brain storm optimization by modifying the well-known brain storm optimization algorithm with inclusion of fractional calculus is proposed. For experimentation, six different patient datasets from UCI machine learning repository are used and the performance is compared with existing method using accuracy and F-measure. The average accuracy and F-measure reached by the proposed method with six different datasets are 89.6 and 88.8%, respectively.