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

• Main Authors: Abd Faris Mohammad, Manaa Mehdi Ebady
• Format: Article
• Language: English
• Published: De Gruyter 2020-11-01
• Series: Open Computer Science
• Subjects: internet of things (iot); big data; map-reduce; hdf; random forest (rf) algorithm
• Online Access: https://doi.org/10.1515/comp-2020-0204

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.