Saravi, F.B.; Moghanian, S.; Javidi, G.; Sheybani, E.O. Machine Learning in Apache Spark Environment for Diagnosis of Diabetes. Preprints2021, 2021110200. https://doi.org/10.20944/preprints202111.0200.v1
APA Style
Saravi, F.B., Moghanian, S., Javidi, G., & Sheybani, E.O. (2021). Machine Learning in Apache Spark Environment for Diagnosis of Diabetes. Preprints. https://doi.org/10.20944/preprints202111.0200.v1
Chicago/Turabian Style
Saravi, F.B., Giti Javidi and Ehsan O Sheybani. 2021 "Machine Learning in Apache Spark Environment for Diagnosis of Diabetes" Preprints. https://doi.org/10.20944/preprints202111.0200.v1
Abstract
Disease-related data and information collected by physicians, patients, and researchers seem insignificant at first glance. Still, the same unorganized data contain valuable information that is often hidden. The task of data mining techniques is to extract patterns to classify the data accurately. One of the various Data mining and its methods have been used often to diagnose various diseases. In this study, a machine learning (ML) technique based on distributed computing in the Apache Spark computing space is used to diagnose diabetics or hidden pattern of the illness to detect the disease using a large dataset in real-time. Implementation results of three ML techniques of Decision Tree (DT) technique or Random Forest (RF) or Support Vector Machine (SVM) in the Apache Spark computing environment using the Scala programming language and WEKA show that RF is more efficient and faster to diagnose diabetes in big data.
Keywords
Diabetes; Diagnosis; Machine Learning; Wireless Body Area Networks; Apache Spark; Feature Selection
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.