An intelligent framework for monitoring dengue fever risk using LDA-ANFIS

Abstract

Dengue fever (DENF) is considered as the world’s fastest-growing vector-borne disease. Its timely and proper treatment possesses great significance, as it affects vital body organs and causes morbidity and mortality. In this paper, a dew-cloud assisted DENF-based cyber physical system (CPS) is proposed. This system diagnoses DENF, and monitors the effects of DENF infection on vital body organs. This system uses Linear Discriminant Analysis-Adaptive Neuro Fuzzy Inference System (LDA-ANFIS) classification at the dew space to diagnose the DENF, and monitor the coronary heart disease (CHD) risk of the DENF infected users in real-time. Based on the real-time diagnosis and monitoring, the system immediately alerts the concerned stakeholders and provides timely medical support in case of any dengue-related emergency. The proposed CPS also assesses the Temporal Network Analysis (TNA)-based DENF outbreak risk, at the cloud space to control and prevent the outbreak of DENF by identifying the critical areas and individuals, which contributes in spreading DENF infection. The experimental evaluation of the proposed system acknowledges performance efficiency and utilization through various experimental and statistical approaches.

Keywords

Dew computing cloud computing coronary heart disease Linear Discriminant Analysis Internet of Things Adaptive Neuro Fuzzy Inference System

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