Predicting Heart Diseases from Large Scale IoT Data Using a Map-Reduce Paradigm

Abstract

Over the last few years, the huge amount of data represented a major obstacle to data analysis. Big data implies that the volume of data undergoes a faster progress than computational speeds, thereby demanding a larger data storage capacity. The Internet of Things (IoT) is a main source of data that is closely related to big data, as the former extends to a variety of fields such as healthcare, entertainment, and disaster control. Despite the different advantages associated with the composition of Big Data analytics and IoT, there are a number of complex difficulties and issues involved that need to be resolved and managed to ensure an accurate data analysis. Some of these solutions include the utilization of map-reduce techniques, processing, and large data scale, particularly for the relatively less time that this method requires to process large data from the Internet of Things. Machine learning algorithms of this kind are often implemented in the healthcare sector. Medical facilities need to be advanced so that more appropriate decisions can be made in terms of patient diagnosis and treatment options. In this work, two datasets have been used: the first set, used in the prediction of heart diseases, obtained an accuracy rate of 84.5 for RF and 83 for J48, whereas the second dataset is related to weather stations (automated sensors) and obtained accuracy rates of 88.5 and 86.5 for RF and J48, respectively.

A systematic literature review on the use of big data analytics in humanitarian and disaster operations

“…This procedure eliminated around 62% of the papers, leaving 160 for full-text review. One example of an article that has been omitted is ‘Predicting Heart Diseases from Large Scale IoT Data Using a Map-Reduce Paradigm’ (Abd & Manaa, 2020 ). While this article does not discuss humanitarian or disaster operations, it was surfaced in the list due to the inclusion of the key terms ‘big data’ and ‘disaster’ in the abstract.…”

Innovative System for Analyzing English Translation Software Based on IoT Big Data

“…However, Chinese does not have a variety of verb forms, so at this stage, the Chinese tagging set of this system is a subset of the English tagging set. e strategy of instance pattern matching is to apply the input sentence and the results of lexical analysis, part-of-speech tagging, and shallow syntactic analysis of the input sentence to match the instance patterns in the library, that is, to calculate the similarity between the two [23]. Select the most basic similar instance pattern in the library as the matching result.…”