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

The management and exploitation of health Information is a demandingtask for health informaticians to provide the highest quality healthcare delivery. Storage, retrieval and interpretation of healthcare information are important stages in health informatics. Consequently, 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 concern, a hybrid model is developed for retrieval of similar cases through the use of Case-based reasoning. Here, new measure called, parametric Enabled-Similarity Measure (PESM) is proposed and a new optimization algorithm called, Fractional Brain Storm Optimization (FBSO), by modifying the well known Brain Storm Optimization (BSO) algorithm with the addition of fractional calculus is proposed. For experimentation, three different patient dataset from UCI machine learning repository is 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 three different dataset is 89.6% and 88.8% respectively.