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

Creative Commons License

BAKIRARAR B., Yüksel C., YAVUZ Y.

Journal of Health Research, cilt.36, ss.334-344, 2022 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1108/jhr-05-2020-0136
  • Dergi Adı: Journal of Health Research
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus
  • Sayfa Sayıları: ss.334-344
  • Anahtar Kelimeler: Big data, Classification, Data mining, Diabetes mellitus, ADHERENCE
  • Ankara Üniversitesi Adresli: Evet

Özet

© 2021, Batuhan Bakirarar, Cemil Yüksel and Yasemin Yavuz.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. Research limitations/implications: Because of the chosen research approach, the research results may lack generalizability. Originality/value: The mahout random forest method provided a better prediction of new prescription requirements than the other methods according to accuracy criteria.