Revealing the nature of cardiovascular disease using DERGA, a novel data ensemble refinement greedy algorithm

Asteris PG 1 , Gavriilaki E 2 , Kampaktsis PN 3 , Gandomi AH 4 , Armaghani DJ 5 , Tsoukalas MZ 1 Show all authors , Avgerinos DV 6 , Grigoriadis S 7 , Kotsiou N 8 , Yannaki E 9 , Drougkas A 10 , Bardhan A 11 , Cavaleri L 12 , Formisano A 13 , Mohammed AS 14 , Murlidhar BR 15 , Paudel S 16 , Samui P 11 , Zhou J 17 , Sarafidis P 18 , Virdis A 19 , Gkaliagkousi E 20

Affiliations 

  • 1 Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
  • 2 2nd Propedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece. Electronic address: elenicelli@yahoo.gr
  • 3 Division of Cardiology, Department of Medicine, Columbia University, New York, NY 10032, United States
  • 4 Faculty of Engineering & IT, University of Technology Sydney, Sydney, NSW 2007, Australia; University Research and Innovation Center (EKIK), Óbuda University, 1034 Budapest, Hungary
  • 5 School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia
  • 6 Onassis Cardiac Surgery Center, Athens, Greece
  • 7 Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
  • 8 2nd Propedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
  • 9 Hematology Laboratory, Theagenion Hospital, Thessaloniki, Greece
  • 10 Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya, Spain
  • 11 Civil Engineering Department, National Institute of Technology Patna, Bihar, India
  • 12 Department of Civil, Environmental, Aerospace and Materials Engineering, University of Palermo, Palermo, Italy
  • 13 Department of Structures for Engineering and Architecture, University of Naples "Federico II", Naples, Italy
  • 14 Engineering Department, American University of Iraq, Sulaimani, Kurdistan-Region, Iraq
  • 15 Institute for Smart Infrastructure & Innovative Construction (ISiiC), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia
  • 16 Department of Civil and Environmental Engineering, University of Nevada, Reno, USA
  • 17 School of Resources and Safety Engineering, Central South University, Changsha 410083, China
  • 18 1st Department of Nephrology, Hippokration Hospital, Aristotle University of Thessaloniki, Greece
  • 19 Professore Ordinario Medicina Interna, Dip. Medicina Clinica e Sperimentale, Università di Pisa, Italy
  • 20 3rd Department of Internal Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
Int J Cardiol, 2024 Jul 03;412:132339.
PMID: 38968972 DOI: 10.1016/j.ijcard.2024.132339

Abstract

BACKGROUND: The study aimed to determine the most crucial parameters associated with CVD and employ a novel data ensemble refinement procedure to uncover the optimal pattern of these parameters that can result in a high prediction accuracy.

METHODS AND RESULTS: Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis). The data were analyzed using an artificial intelligence-based algorithm with the primary objective of identifying the optimal pattern of parameters that define CVD. The study highlights the effectiveness of a six-parameter combination in discerning the likelihood of cardiovascular disease using DERGA and Extra Trees algorithms. These parameters, ranked in order of importance, include Platelet-derived Microvesicles (PMV), hypertension, age, smoking, dyslipidemia, and Body Mass Index (BMI). Endothelial and erythrocyte MVs, along with diabetes were the least important predictors. In addition, the highest prediction accuracy achieved is 98.64%. Notably, using PMVs alone yields a 91.32% accuracy, while the optimal model employing all ten parameters, yields a prediction accuracy of 0.9783 (97.83%).

CONCLUSIONS: Our research showcases the efficacy of DERGA, an innovative data ensemble refinement greedy algorithm. DERGA accelerates the assessment of an individual's risk of developing CVD, allowing for early diagnosis, significantly reduces the number of required lab tests and optimizes resource utilization. Additionally, it assists in identifying the optimal parameters critical for assessing CVD susceptibility, thereby enhancing our understanding of the underlying mechanisms.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

Similar publications