Discovering Prescription Patterns in Type 2 Diabetes Based on Demographic Attributes Using Association Rules

Putri Yani; Maulida Hikmah; Deni Mahdiana

doi:10.24014/ijaidm.v8i3.38082

Discovering Prescription Patterns in Type 2 Diabetes Based on Demographic Attributes Using Association Rules

Putri Yani, Maulida Hikmah, Deni Mahdiana

Abstract

Type 2 diabetes mellitus (T2DM) is a chronic disease that requires effective long-term therapeutic management. Appropriate and continuous treatment is crucial to prevent complications and improve patients’ quality of life. In clinical practice, prescription patterns vary significantly and are influenced by demographic and clinical characteristics. This study aimed to analyze prescription patterns of T2DM patients based on demographic and clinical attributes, and to identify frequently co-prescribed drug combinations using the Apriori algorithm. A total of 3,500 prescription records were obtained from RSUD H. Damanhuri Barabai. The analysis was conducted in two stages: (1) association between demographic factors (age, gender, blood pressure) and prescribed drugs, and (2) association among drugs regardless of patient demographics. With minimum support of 3%, confidence thresholds of 60% and 35%, and lift greater than 1.5, fifteen valid rules were identified in the demographic-to-drug analysis, and nine rules in the drug combination analysis. Strong patterns were observed, such as the prescription of Empagliflozin and Insulin Degludec for hypertensive patients aged 40–49, and the co-prescription of Acarbose and Glimepiride. These findings demonstrated that the Apriori algorithm was effective in identifying meaningful prescription patterns. Beyond methodological contributions, the results provide practical value for hospitals by supporting pharmacy managers in drug procurement planning, optimizing stock management, and designing distribution strategies that anticipate patient needs based on prescription trends.

Keywords

Apriori; Data Mining; Demographic Analysis; Diabetes Mellitus; Prescription Patterns

Full Text:

PDF

References

Johnson EL, Feldman H, Butts A, Chamberlain J, Collins B, Doyle-Delgado K, et al. Standards of medical care in diabetes—2020 abridged for primary care providers. Clin Diabetes. 2020;38(1):10–38.

IDF. International Diabetes Federation. Diabetes Research and Clinical Practice. 2021.

Kementerian Kesehatan RI. Laporan Nasional Riskesdas 2018 [Internet]. Lembaga Penerbit Balitbangkes. 2018. p. hal 156. Available from: https://repository.badankebijakan.kemkes.go.id/id/eprint/3514/1/Laporan Riskesdas 2018 Nasional.pdf

Adi S. Pengelolaan Dan Pencegahan Diabetes Melitus Tipe 2 Dewasa di Indonesia. PB Perkeni. 2019;133.

Susanto Y, Bangun Lestari S, Purwati E. Pola Peresepan Obat Antidiabetik Pada Pasien Diabetes Melitus Tipe 2 Di Poli Penyakit Dalam Rs “X” Sidoarjo Periode Oktober- Desember 2019. 2020;1(2):69–80.

Artini KS, Ajeng T, Saifana CS. Gambaran Penggunaan Antidiabetes Oral Pada Pasien Diabetes Mellitus Tipe 2. SIKesNas. 2022;2045:333–40.

Jiang L, Chang J, Wang Y, Ma X. Pharmacological therapies for type 2 diabetes: a review of clinical trials and real-world studies. J Clin Med. 2020;9(7):1–20.

Perkumpulan Endokrinologi Indonesia. Pedoman Pengelolaan dan Pencegahan Diabetes Melitus Tipe 2 di Indonesia 2021. PB Perkeni; 2021.

Astuti IW, Suryadi T. Faktor-faktor yang mempengaruhi pola resep obat pada pasien diabetes melitus. J Farm Klin Indones. 2020;9(1):25–32.

Mohan V, Deepa M, Pradeepa R, Anjana RM. Therapeutic options for diabetes in low- and middle-income countries. Lancet Diabetes Endocrinol. 2019;7(1):1–12.

Saputra R, Sibarani AJP. Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Meningkatkan Pola Penjualan Obat. JATISI (Jurnal Tek Inform dan Sist Informasi). 2020;7(2):262–76.

Nola R, Suswaini E, Pebriadi W. Penerapan Association Rule Menggunakan Algoritma Apriori Pada Poliklinik Penyakit Dalam (Studi Kasus: Rumah Sakit Umum Daerah Bintan). J Sains dan Inform. 2021;7(2):222–30.

Kumar R, Singh R, Jain A. Apriori Algorithm for Finding Frequent Itemsets in Big Data. J Comput Sci. 2018;14(2):120–7.

Tandel KR, Patel A, Thakkar S. Use of association rule learning in clinical prescription patterns: A review. Int J Heal Med Sci. 2020;10(2):67–74.

Azevedo A, Santos MF. Data Mining and CRISP-DM Methodology. In: Big Data Processing and Applications. Springer, Cham; 2018. p. 245–64.

Chapman P, Clinton J, Kerber R, Khabaza T, Reinartz T, Shearer C, et al. CRISP-DM 1.0 Step-by-Step Data Mining Guide: Revisited for Big Data Applications. SPSS; 2020.

Kastouni MZ, Ait Lahcen A. Big data analytics in telecommunications: Governance, architecture and use cases. J King Saud Univ - Comput Inf Sci. 2022;34(6):2758–70.

Han J, Pei J, Kamber M. Data Mining: Concepts and Techniques. 3rd Edition. Morgan Kaufmann; 2019.

García S, Luengo J, Herrera F. Data Preprocessing in Data Mining. Vol. 72. 2015.

Rahm E, Do H. Data cleaning: Problems and current approaches. IEEE Data Eng Bull [Internet]. 2000;23(4):3–13. Available from: http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/dw/paper/data_cleaning.pdf%5Cnpapers2://publication/uuid/17B58056-3A7F-4184-8E8B-0E4D82EFEA1A%5Cnhttp://dc-pubs.dbs.uni-leipzig.de/files/Rahm2000DataCleaningProblemsand.pdf

Guyon I. An Introduction of Variabel and Feature Selection. CrossRef List Deleted DOIs. 2015;1(April).

Richesson RL. An informatics framework for the standardized collection and analysis of medication data in networked research. J Biomed Inform [Internet]. 2014;52:4–10. Available from: http://dx.doi.org/10.1016/j.jbi.2014.01.002

Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules in Large Databases. In: Very Large Data Bases Conference [Internet]. 1994. Available from: https://api.semanticscholar.org/CorpusID:3131928

Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases. SIGMOD Rec [Internet]. 1993 Jun;22(2):207–216. Available from: https://doi.org/10.1145/170036.170072

Tan PN, Steinbach M, Kumar V. Introduction to Data Mining. 2nd ed. Pearson; 2018.

Altaf W, Shahbaz M, Guergachi A. Applications of association rule mining in health informatics: a survey. Artif Intell Rev [Internet]. 2017;47(3):313–40. Available from: https://doi.org/10.1007/s10462-016-9483-9

Mahmood S, Shahbaz M, Guergachi A. Negative and positive association rules mining from text using frequent and infrequent itemsets. Sci World J. 2014;2014.

Hsun-Lu P, Keng JL, Tsai FM, Lu PH, Kou CY. An Apriori Algorithm‐Based Association Rule Analysis to.pdf. 2021.

American Diabetes Association. Standards of Medical Care in Diabetes—2019. Diabetes Care. 2019;42(Suppl 1):S1--S193.

Wallard L. Model deployment of a predictive machine learning model for clinical decision support. Bachelor Thesis, Leiden Inst Adv Comput Sci. 2023;

Kautzky-Willer A, Harreiter J. Sex and gender differences in therapy of type 2 diabetes. Diabetes Res Clin Pract [Internet]. 2017;131(July):230–41. Available from: http://dx.doi.org/10.1016/j.diabres.2017.07.012

World Health Organization. Global report on diabetes. Geneva: World Health Organization; 2020.

Joint National Committee. The seventh report of the joint national committee on the prevention, detection, evaluation and treatment of high blood pressure: The weapons are ready. 2004.

DOI: http://dx.doi.org/10.24014/ijaidm.v8i3.38082

Refbacks

There are currently no refbacks.

Office and Secretariat:

Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau

Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone: 085275359942

Journal Indexing:

IJAIDM Stats