Observational Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Methodol. Jun 20, 2024; 14(2): 92608
Published online Jun 20, 2024. doi: 10.5662/wjm.v14.i2.92608
Discovering hidden patterns: Association rules for cardiovascular diseases in type 2 diabetes mellitus
Pradeep Kumar Dabla, Kamal Upreti, Dharmsheel Shrivastav, Vimal Mehta, Divakar Singh
Pradeep Kumar Dabla, Dharmsheel Shrivastav, Department of Biochemistry, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research, Delhi 110002, India
Kamal Upreti, Department of Computer Science, CHRIST, Ghaziabad 201003, India
Vimal Mehta, Department of Cardiology, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research, Delhi 110002, India
Divakar Singh, Barkatullah University Institute of Technology, Barkatullah University, Bhopal 462026, India
Author contributions: Dabla PK designed the research; Dabla PK and Shrivastav D performed the research; Mehta V contributed to patient clinical data & sample collection; Upreti K and Shrivastav D contributed analytic tools and analyzed the data; Upreti K, Singh D, and Dabla PK wrote the paper; all authors have accepted responsibility for the entire content of this manuscript, and reviewed and approved its submission.
Institutional review board statement: The study was approved by the institutional ethical committee of Maulana Azad Medical College and associated hospitals, Delhi, India (F1/IEC/MAMC/85/03/21/no.422; Dt-30.08.2021).
Informed consent statement: All patients gave informed consent prior to the participation in the study.
Conflict-of-interest statement: No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article. All authors declare no conflict of interest for this article.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at pradeep_dabla@yahoo.com.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Pradeep Kumar Dabla, MD, Professor, Department of Biochemistry, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research, Jawaharlal Nehru Marg, New Delhi, Delhi 110002, India. pradeep_dabla@yahoo.com
Received: January 30, 2024
Revised: March 15, 2024
Accepted: April 9, 2024
Published online: June 20, 2024
Processing time: 135 Days and 16.2 Hours
Abstract
BACKGROUND

It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD), and studies are able to correlate their relationships with available biological and clinical evidence. The aim of the current study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features relevant to these diseases. ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care.

AIM

To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery.

METHODS

This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi, involving a total of 300 consented subjects categorized into three groups: CAD with diabetes, CAD without diabetes, and healthy controls, with 100 subjects in each group. The participants were enrolled from the Cardiology IPD & OPD for the sample collection. The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value.

RESULTS

The clinical dataset comprised 35 attributes from enrolled subjects. The analysis produced rules with a maximum branching factor of 4 and a rule length of 5, necessitating a 1% probability increase for enhancement. Prominent patterns emerged, highlighting strong links between health indicators and diabetes likelihood, particularly elevated HbA1C and random blood sugar levels. The ARM technique identified individuals with a random blood sugar level > 175 and HbA1C > 6.6 are likely in the “CAD-with-diabetes” group, offering valuable insights into health indicators and influencing factors on disease outcomes.

CONCLUSION

The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes. Implying artificial intelligence techniques with medical data, we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.

Keywords: Coronary artery disease, Type 2 diabetes mellitus, Coronary angiography, Association rule mining, Artificial intelligence

Core Tip: The study is aimed to apply association rule mining (ARM) to discover the correlation between type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD). ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms. It will assist to optimise the decision-making in patient care by providing new insights into multivariate pattern discovery. Thus, this research facilitates targeted medication for patients to regulate uncontrolled clinical parameters in case of CAD with T2DM.