Big Data Analytics for Diabetes Prediction on Apache Spark

Document Type : Original Article

Authors

1 Department of Computer Science and Engineering Faculty of Electronic Engineering menofia-Egypt

2 Department of Computer Science and Engineering Faculty of Electronic Engineering, menouf, Egypt

Abstract

Dangerous diseases like diabetes, in which blood glucose levels are too high, some machine learning models have been used to classify or predict the patient state. Currently, the collected dataset size increases dramatically. Therefore, big data analytics technology is an essential factor in building an efficient healthcare system that can fit for the future. This paper discusses the effect of using big data analytics with different dataset sizes by usinga  different number of processing cores over apache spark. The system has been evaluated using several performance evaluation metrics like accuracy, recall, precision, time, etc. A comparative study made among various algorithms such as Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF). The experimental result shows that the most accurate models were when using RF, and SVM, and the minimum time model was when using NB algorithm.

Keywords

References

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Menoufia Journal of Electronic Engineering Research
Volume 28, ICEEM2019-Special Issue
ICEEM2019-Special Issue: 1st International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 7-8 Dec.
2019
Pages 355-360

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