Causal Relationship of Factors Influencing Heart Disease and Diabetes Using the GES Algorithm

Nurhaeka Tou, Putri Mentari Endraswari, Iski Zaliman

Abstract


Diabetes and heart disease are leading causes of global death, and if not properly managed, can be fatal. Previous studies have explored the factors influencing these two diseases through correlation analysis, multivariate analysis, machine learning, and deep learning. However, these approaches generally only identify associations without being able to predict causal relationships. This study aims to model the causal relationships between factors in two clinical datasets: heart disease (13 parameters) and diabetes (9 parameters), in order to support early diagnosis and prevention. The Greedy Equivalence Search (GES) algorithm is used to determine the direction of the causal relationship between parameters. The results show that heart disease exhibits three directional relationships: between blood pressure and age, between Maximum Heart Rate (MHR) and age, and between age and cholesterol. Then, diabetes exhibits two bidirectional relationships: blood pressure and BMI, then BMI and Diabetes Pedigree Function. In addition, diabetes also exhibits three directional relationships: Diabetes Pedigree Function and Glucose, BMI and Glucose, and Blood Pressure and Glucose. Thus, it can be concluded that the algorithm can identify the causal relationship between diabetes and heart disease.

Keywords


Causality; Diabetes; Greedy Equivalence Search; Heart; Modelling

Full Text:

PDF

References


N. Afrianto et al., “Applying PC Algorithm and GES to Three Clinical Data Sets : Heart Disease , Diabetes , and Hepatitis,” in IOP Conference Series: Materials Science and Engineering, 2021, pp. 1–8.

M. Bakator and D. Radosav, “Deep Learning and Medical Diagnosis : A Review of Literature,” Multimodal Technol. Interact., vol. 02, no. 03, pp. 1–12, 2018.

M. Fatima and M. Pasha, “Survey of Machine Learning Algorithms for Disease Diagnostic,” J. Intell. Learn. Syst. Appl., vol. 9, no. 1, pp. 1–16, 2017.

B. Y. J. Pearl, “The Seven Tools of Causal Inference , with Reflections on Machine Learning,” Commun. ACM, vol. 62, no. 3, pp. 54–60, 2019.

S. Gupta, W. Zhang, and F. Wang, “Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study IEEE 16 th,” in Int. Conf. on Data Mining (ICDM), 2016, no. 171–180.

R. Rahmadi, P. Groot, M. Heins, H. Knoop, T. Heskes, and T. Optimistic, “Causality on Cross-Sectional Data : Stable Specification Search in Constrained Structural Equation Modeling,” Appl. Soft Comput., vol. 52, pp. 687–698, 2017.

X. Chen, “Transmission Line Outage Probability Prediction Under Extreme Events Using Peter-Clark Bayesian Structural Learning.”

N. Tou, C. Effendy, and R. Rahmadi, “Causal Relations of Factors Representing the Elderly Independence in Doing Activities of Daily Livings Using S3C-Latent Algorithm,” Int. J. Artif. Intell. Res., vol. 5, no. 1, pp. 65–77, 2021.

M. Pasquato and B. L. Davis, “Causa prima : cosmology meets causal discovery for the first time,” Macnwsjfjf, 2023.

M. Z. Naser, “Discovering causal models for structural , construction and defense- related engineering phenomena,” Def. Technol., vol. 43, pp. 60–79, 2025.

Y. Setiya, R. Nur, A. Sa, D. Aldo, and B. Masulah, “Causal Modeling of Factors in Stunting using the Peter-Clark and Greedy Equivalence Search Algorithms,” J. Ilmu Pengetah. dan Teknol. Komput., vol. 10, no. 3, pp. 523–533, 2025.

N. Afrianto et al., “Applying PC Algorithm and GES to Three Clinical Data Sets: Heart Disease, Diabetes, and Hepatitis,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1077, no. 1, p. 012067, 2021.

G. Nursa, Y. Fauzi, and J. Habibi, “Faktor- Faktor Yang Mempengaruhi Kejadian Diabetes Melitus Di Puskesmas Bintuhan Kabupaten Kaur Tahun 2022,” J. Hygeia Public Heal., vol. 1, no. 1, pp. 1–6, 2022.

S. A. Muna Lubis, T. N. F. Aminah, S. Pangestuty, R. Atika, S. P. Sembiring, and Z. Aidha, “Faktor-Faktor yang Berhubungan dengan Kejadian Diabetes Melitus (DM) di Desa Kubah Sentang Kecamatan Pantai Labu,” J. Ilm. Univ. Batanghari Jambi, vol. 23, no. 2, p. 2061, 2023.

R. D. Christy, H. Lukman, and H. Karnirius, “Faktor-Faktor Yang Mempengaruhi Kejadian Penyakit Jantung Koroner Di Rsud Rantau Prapat Tahun 2020,” PREPOTIF J. Kesehat. Masy., vol. 5, no. 2, 2021.

V. B. Valentine and I. A. Rakhmawati, “Analisis Faktor-Faktor Yang Mempengaruhi Kejadian Penyakit Jantung Koroner Pada Anggoita POLRI MAPOLDA Jawa Timur Tahun 2024,” J. Manaj. dan Adm. Rumah Sakit Indones., vol. 8, no. 2, pp. 224–232, 2024.

P. K. Whelton et al., “Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults,” J. Am. Coll. Cardiol., vol. 71, no. 19, pp. e127–e248, 2018.

E. J. Benjamin et al., Heart Disease and Stroke Statistics-2019 Update, vol. 139, no. 10. 2019.

D. A. Saputri and A. Novitasari, “Hubungan Usia Dengan Kadar Kolesterol Masyarakat Di Kota Bandar Lampung,” BIOEDUKASI (Jurnal Pendidik. Biol., vol. 12, no. 2, p. 238, 2021.

M. Bertolotti, G. Lancellotti, and C. Mussi, “Changes in cholesterol homeostasis associated with aging and with age-related conditions : pathophysiological and clinical implications,” Res. Gerontol. Geriatr., pp. 32–42, 2024.

A. A. Lestari, E. Haryanto, Museyaroh, and A. Handayati, “No Title,” Anal. Kesehat. Sains, vol. 13, no. 1, pp. 20–24, 2024.

T. P. Utami, “Hubungan antar Indeks Massa Tubuh dan Tekanan Darah pada Pasien Diabetes Melitus Tipe 2,” J. Arch. Pharm., vol. 1, no. 1, pp. 19–22, 2019.

R. Between, B. Mass, and I. N. T. H. E. Elderly, “Relationship Between Body Massa Index and Blood Pressure in the Elderly,” J. Kesehat. Manarang, vol. 10, no. 3, pp. 329–335, 2024.

S. F. Jannah, Wantonoro, and Widiastuti, “Korelasi indeks massa tubuh dengan gula darah pada pasien diabetes melitus,” in Prosiding Seminar Nasional Penelitian dan Pengabdian kepada Masyarakat LPPM Universitas Aisyiyah Yogyakarata, 2025, vol. 3, pp. 807–814.

C. K. Lariwu, C. P. Sarayar, L. Pondaag, G. Merentek, and M. E. Lontaan, “Indeks Masa Tubuh , Riwayat Keluarga dan Kebiasaan Konsumsi Gula : Faktor Dominan Penyebab Diabetes Melitus Tipe 2 Pada Lanjut Usia Di Kota Tomohon Abstrak,” J. Ilmu Pendidik. Nonform., vol. 10, no. January, pp. 379–386, 2024.




DOI: http://dx.doi.org/10.38101/sisfotek.v15i2.15992

Refbacks

  • There are currently no refbacks.


 

JURNAL SISFOTEK GLOBAL

Organized by: Research Center and Community Development
Published by: Institut Teknologi dan Bisnis Bina Sarana Global
Jl. Aria Santika No.43A, Margasari, Kec. Karawaci, Kota Tangerang, Banten 15114
Phone. +62 552 2727
Email: lppm@global.ac.id

INDEXED BY:

   


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License..
Based on a work at https://journal.global.ac.id/index.php/sisfotek/index.