Case Retrieval Algorithm Using Similarity Measure and Fractional Brain Storm Optimization for Health Informaticians
Poonam Yadav
Department of Computer Science and
Engineering, DAV College of Engineering and Technology, Maharshi Dayanand
University, India
Abstract: 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.
Keywords: Case-based reasoning, case retrieval, optimization,
similarity, fractional calculus.