An Efficient Named Entity Recognition Model in Medical Health Records using Water Wave Optimization with Kernel Extreme Learning Machine

Abstract

Named entity recognition (NER) become a significant domain for several natural language processing techniques, namely information extraction, information retrieval, etc. The benefits of NER have attracted more attention among researchers in diverse fields. But the class labels and extension of named entities considerably differ with respect to distinct application domains. Medical literature includes valuable details namely clinical signs, diagnosis, drug, and medication for particular diseases. As the knowledge gaining from medical literature is a tedious task, this paper aims to develop a new NER model in medical literature using water wave optimization (WWO) with kernel extreme learning machine (KELM), called WWO-KELM. The presented WWO-KELM model involves three major stages namely pre-processing, classification, and parameter tuning. The presented model performs preprocessing to convert the raw medical data into a useful format. In addition, KELM model is executed to perform classification process. Finally, the two main parameters of KELM such as penalty parameter ‘C’ and kernel bandwidth ‘γ’ of the KELM are computed using WWO algorithm. An extensive set of simulations were carried out to determine the effectual classification performance of the WWO-KELM model. The resultant experimental values ensured the betterment of the WWO-KELM model over the existing methods.