A Novel Big Data Handling Approach Using Fuzzy Rule Based Artificial Neural Network

Abstract

The only criteria used in the past for making healthcare decisions were the doctor's experience in the area, the patient's symptoms, and the results of the diagnostic tests. Applications for remote healthcare are being used by more people as a result of advancements in information and communication technology (ICT). The patient data that is obtained through these applications varies in terms of quantity, velocity, variety, veracity, and usefulness. Processing such a substantial volume of various data is one of the key problems that necessitates a certain technique. This method offers a new way for handling big data that uses a Fuzzy rule-based classifier to categorize the big data generated in this environment for healthcare services. This paper presents A novel big data handling approach using Fuzzy Rule based Artificial Neural Network (FRANN). Information collected from a network of body sensors, a specific automotive network, and network of hospitals. Collected data is preprocessed using the principal component analysis algorithm. The presented technique utilizes a clustering strategy based on fuzzy rules. Various metrics such as response time, accuracy, classification time, Recall and Precision are used to measure the proposed scheme. Standard techniques are correlated with the outcomes acquired to check the level of effectiveness of the respected scheme.