Batuhan Bakırarar, Cemil Yüksel and Yasemin Yavuz
The study aimed to evaluate the effectiveness of using large data sets for new diabetes patient prescriptions.
Abstract
Purpose
The study aimed to evaluate the effectiveness of using large data sets for new diabetes patient prescriptions.
Design/methodology/approach
This study consisted of 101,766 individuals, who had applied to the hospital with a diabetes diagnosis and were hospitalized for 1–14 days and subjected to laboratory tests and medication.
Findings
With the help of Mahout and Scala, data mining methods of random forest and multilayer perceptron were used. Accuracy rates of these methods were found to be 0.879 and 0.849 for Mahout and 0.849 and 0.870 for Scala.
Originality/value
The mahout random forest method provided a better prediction of new prescription requirements than the other methods according to accuracy criteria.