Prediction of new prescription requirements for diabetes patients using big data technologies

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.

Keywords

• big data
• classification
• data mining
• diabetes mellitus