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Machine Learning Supported Diabetes Prediction with Apache Spark

Diabetes is one of the critical health problems that affect the organs of the human body. Therefore, diabetes is recognized as a global health problem in the 21st century. To avoid the problems that arise as a result of the diabetes and to treat it before it worsen, there is a need for a system that can predict and process diabetes. In recent years, various technological tools and applications have been used for the early diagnosis of many diseases in the field of health. One of these applications is to perform analyzes for early diagnosis of the disease with the help of data mining and machine learning techniques. In this study, diabetes analyzes are carried out with Apache Spark technology, which has been very popular in big data processing recently. So, the performances of five different machine learning classification algorithms in the Apache Spark MLlib library used for prediction in the analysis are compared and it is seen that the Random Forest (RO) algorithm has the best performance. The results of the analyzes show that the Apache Spark technology used can be used to detect such health problems.