Identification of Diabetes Mellitus Risk Factors With a Data Mining Classification Approach

Ade Agustina, Galih Ady Permana, Christina Juliane

Abstract


Diabetes mellitus is a chronic disease characterized by an increase in the frequency of eating, drinking and urinating due to the failure of the process of sugar entering the body to be converted into energy due to the pancreas function not being able to produce enough insulin or not producing insulin at all. The purpose of writing this paper is to test the accuracy of the decision tree and rules generated by the ID3 algorithm and correlate it with literature studies from research that has been carried out by researchers in the health sector related to diabetes and the results of this classification are expected to be used as a reference. For everyone to be able to change their lifestyle to avoid the risk of developing diabetes mellitus by looking at the attributes of the dataset. In this study, the application of data mining with the classification method with the ID3 algorithm using datasets from the BRFSS survey results was carried out. The results of data testing can be obtained from the accuracy of the rules generated by the ID3 algorithm with an accuracy rate of 85.95%. The rules generated by the ID3 algorithm are also correlated with the literature from research that has been carried out by researchers in the health sector, and the results are that the rules generated from the attribute indicators of the dataset have relevance and suitability


Keywords


Algoritma ID3;Data mining;Dataset;Decision tree;Diabetes melitus;

Full Text:

PDF

References


Kementrian Kesehatan Republik Indonesia, "DIREKTORAT PENCEGAHAN DAN PENGENDALIAN PENYAKIT TIDAK MENULAR," Diabetes :Penderita di Indonesia bisa mencapai 30 juta orang pada tahun 2030, 11 December 2018. [Online]. Available: http://p2ptm.kemkes.go.id/tag/diabetes-penderita-di-indonesia-bisa-mencapai-30-juta-orang-pada-tahun-2030. [Accessed 27 July 2022].

C. INDONESIA, "Indonesia Masuk 5 Besar Negara Kasus Diabetes Tertinggi di Dunia," 06 December 2021. [Online]. Available: https://www.cnnindonesia.com/gaya-hidup/20211206080008-255-730258/indonesia-masuk-5-besar-negara-kasus-diabetes-tertinggi-di-dunia. [Accessed 27 July 2022].

W. M. P. Muhammad Abid Wiratama, "OPTIMASI ALGORITMA DATA MINING MENGGUNAKAN BACKWARD ELIMINATION UNTUK KLASIFIKASI PENYAKIT DIABETES," Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI, vol. Volume 11, no. Nomor 1 , pp. 1 - 12, 2022.

L. Y. M. R. B. Waode Azfari Azis, "HUBUNGAN ANTARA TINGKAT PENGETAHUAN DENGAN GAYA HIDUP PADA PENDERITA DIABETES MELITUS," Jurnal Penelitian Perawat Profesional , vol. Volume 2 , no. Nomor 1, pp. 105 - 114, 2020.

D. W. Hestiana, "FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN KEPATUHAN DALAM PENGELOLAAN DIET PADA PASIEN RAWAT JALAN DIABETES MELLITUS TIPE 2 DI KOTA SEMARANG," Jurnal of Health Education , vol. Volume 2, no. Nomor2, pp. 138 - 124, 2018.

H. F. Y. Melisa Enni Fitriyanti, "PENGALAMAN PENDERITA DIABETES MELLITUS DALAM PENCEGAHAN ULKUS DIABETIK," Jurnal Keperawatan Muhammadiyah Bengkulu, vol. Volume 07, no. Nomor 02, pp. 597 -603, 2019.

G. D. M. Zulma, Angelika and N. Chamidah, "Perbandingan Metode Klasifikasi Naive Bayes, Decision Tree Dan KNearest Neighbor Pada Data Log Firewall," in Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya (SENAMIKA), Jakarta, 2021.

T. Setiyorini and R. T. Asmono, "KOMPARASI METODE DECISION TREE, NAIVE BAYES DAN K-NEAREST NEIGHBOR PADA KLASIFIKASI KINERJA SISWA," Jurnal TECHNO Nusa Mandiri , vol. Vol.15, no. No. 2 , pp. 85 - 92, 2018.

S. Wahyuningsih and D. R. Utari, "Perbandingan Metode K-Nearest Neighbor, Naïve Bayes dan Decision Tree untuk Prediksi Kelayakan Pemberian Kredit," in Konferensi Nasional Sistem Informasi 2018, STMIK Atma Luhur Pangkalpinang, 2018.

D. Marutho, "PERBANDINGAN METODE NAÏVE BAYES, KNN, DECISION TREE PADA LAPORAN WATER LEVEL JAKARTA," Jurnal Ilmiah Infokam, vol. Vol. 15, no. No. 2, pp. 90 - 97, 2019.

Cut Putri Arianie, BUKU PINTAR KADER POSBINDU PTM, Jakarta Selatan: Kementrian Kesehatan Republik Indonesia, 2019.

Nadiah, S. Soim and Sholihin, "Implementation of Decision Tree Algorithm Machine Learning in Detecting Covid-19 Virus Patients Using Public Datasets," Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM) , vol. Vol 5, no. No.1, p. 37 – 43 , 2022.

Suyanto, Data Mining Untuk Klasifikasi dan Klasterisasi Data, Bandung: Informatika, 2019.

Center for Disease Control and Prevention (CDC), "Behavioral Risk Factor Surveillance System," About BRFSS, 16 May 2014. [Online]. Available: https://www.cdc.gov/brfss/about/index.htm. [Accessed 27 July 2022].

R. T. Wulandari, Buku Data Mining Teori dan Aplikasi Rapidminer, Yogyakarta: Gava Media, 2017.

B. Santoso, Teknik Pemanfaatan Data untuk Keperluan Bisnis, Yogyakarta: Graha Ilmu, 2007.

HadiSantoso, HilyahMagdalena and HelnaWardhana, "AplikasiDynamicClusterpadaK-MeansBerbasisWebuntukKlasifikasi DataIndustriRumahan," Matrik: JurnalManajemen,TeknikInformatika,dan RekayasaKomputer, vol. Vol. 21, no. No. 3, pp. 541 - 554, 2022.

Halodoc, "Adakah Hubungan Diabetes dengan Hipertensi? Begini Penjelasannya," 06 January 2022. [Online]. Available: https://www.halodoc.com/artikel/adakah-hubungan-diabetes-dengan-hipertensi-begini-penjelasannya. [Accessed 27 July 2022].

R. Anggraini, "KORELASI KADAR KOLESTEROL DENGAN KEJADIAN DIABETES MELLITUS TIPE 2 PADA LAKI-LAKI," Medical and Health Science Journal, vol. Vol.2, no. No.2, pp. 55 - 60, 2018.

D. A. R. I. Mala Azitha, "Hubungan Aktivitas Fisik dengan Kadar Glukosa Darah Puasa pada Pasien Diabetes Melitus yang Datang ke Poli Klinik Penyakit Dalam Rumah Sakit M. Djamil Padang," Jurnal Kesehatan Andalas, vol. Vol 7, no. No 3, pp. 400 - 404, 2018.

A. A. Utomo, A. A. R, S. Rahmah and R. Amalia, "FAKTOR RISIKO DIABETES MELLITUS TIPE 2:A SYSTEMATIC REVIEW," AN-Nur: Jurnal Kajian dan Pengembangan Kesehatan Masyarakat, vol. Vol. 01, no. Nomor 01, pp. 44 - 52, 2020.

N. D. W. Mildawati, "HUBUNGAN USIA, JENIS KELAMIN DAN LAMA MENDERITADIABETESDENGANKEJADIAN NEUROPATIPERIFERDIABETIK," Caring Nursing Journal, vol. Vol. 3, no. No. 2, pp. 31 - 37, 2019.




DOI: http://dx.doi.org/10.24014/ijaidm.v5i2.18841

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

Click Here for Information


Journal Indexing:

Google Scholar | ROAD | PKP Index | BASE | ESJI | General Impact Factor | Garuda | Moraref | One Search | Cite Factor | Crossref | WorldCat | Neliti  | SINTA | Dimensions | ICI Index Copernicus 

IJAIDM Stats