Intelligent Intuitionistic Fuzzy with Elephant Swarm Behaviour Based Rule Pruning for Early Detection of Alzheimer in Heterogeneous Multidomain Datasets

Abstract

In this Digital age, a rapid advancement in rise of both communication and information technologies provide many services by incorporating intelligent system of automated health assessment of resident welfare. This is achieved by tracking elderly persons activities using smart home technologies and with this activity-based learning, it helps to discover individuals suffering from early stage of Alzheimer can be predicted without distributing their living style. The main purpose of this paper is to use two different domains of datasets for predicting the alzheimer’s in elderly person during its initial stage. Thus, this work uses ubiquitous computing technologies like smart home dataset which collects the daily activities of individuals and as the clinical dataset for prediction of alzheimer’s. The objective of this proposed work is to handle the hesitancy of uncertainty by introducing intelligent intuitionistic fuzzy classifier, which inhibits irrelevant rule generation by acquiring the knowledge of elephant swarm behavior (IIF-ESB). Using elephant swarm search behavior, the rules generated by intuitionistic fuzzy are finetuned to ovoid overfitting problem and thus it eliminates the irrelevant rules. The selected potential rules highly influence the accuracy rate of the prediction model in presence of uncertainty. Performance result of the proposed model (IIF-ESB) proved that with the ability to handle the impreciseness in prediction of alzheimer’s, the usage of degree of hesitancy and intelligent of elephant swarm searching behaviour increases the accurate prediction rate and decrease the misclassification rate considerably while compared with existing prediction models.