Big Data Analytics for Diabetes Prediction on Apache Spark

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.