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

Asteris, PG; Gavriilaki, E; Kampaktsis, PN; Gandomi, AH; Armaghani, DJ; Tsoukalas, MZ; Avgerinos, DV; Grigoriadis, S; Kotsiou, N; Yannaki, E; Drougkas, A; Bardhan, A; Cavaleri, L; Formisano, A; Mohammed, AS; Murlidhar, BR; Paudel, S; Samui, P; Zhou, J; Sarafidis, P; Virdis, A; Gkaliagkousi, E

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

Asteris, PG Gavriilaki, E Kampaktsis, PN Gandomi, AH Armaghani, DJ Tsoukalas, MZ Avgerinos, DV Grigoriadis, S Kotsiou, N Yannaki, E Drougkas, A Bardhan, A Cavaleri, L Formisano, A Mohammed, AS Murlidhar, BR Paudel, S Samui, P Zhou, J Sarafidis, P Virdis, A Gkaliagkousi, E

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Publisher:: ELSEVIER IRELAND LTD
Publication Type:: Journal Article
Citation:: Int J Cardiol, 2024, 412, pp. 132339
Issue Date:: 2024-10-01

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Field	Value	Language
dc.contributor.author	Asteris, PG
dc.contributor.author	Gavriilaki, E
dc.contributor.author	Kampaktsis, PN
dc.contributor.author	Gandomi, AH
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Tsoukalas, MZ
dc.contributor.author	Avgerinos, DV
dc.contributor.author	Grigoriadis, S
dc.contributor.author	Kotsiou, N
dc.contributor.author	Yannaki, E
dc.contributor.author	Drougkas, A
dc.contributor.author	Bardhan, A
dc.contributor.author	Cavaleri, L
dc.contributor.author	Formisano, A
dc.contributor.author	Mohammed, AS
dc.contributor.author	Murlidhar, BR
dc.contributor.author	Paudel, S
dc.contributor.author	Samui, P
dc.contributor.author	Zhou, J
dc.contributor.author	Sarafidis, P
dc.contributor.author	Virdis, A
dc.contributor.author	Gkaliagkousi, E
dc.date.accessioned	2025-01-15T05:42:25Z
dc.date.available	2024-07-02
dc.date.available	2025-01-15T05:42:25Z
dc.date.issued	2024-10-01
dc.identifier.citation	Int J Cardiol, 2024, 412, pp. 132339
dc.identifier.issn	0167-5273
dc.identifier.issn	1874-1754
dc.identifier.uri	http://hdl.handle.net/10453/183636
dc.description.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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ELSEVIER IRELAND LTD
dc.relation.ispartof	Int J Cardiol
dc.relation.isbasedon	10.1016/j.ijcard.2024.132339
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1102 Cardiorespiratory Medicine and Haematology, 1117 Public Health and Health Services
dc.subject.classification	Cardiovascular System & Hematology
dc.subject.classification	3201 Cardiovascular medicine and haematology
dc.subject.mesh	Humans
dc.subject.mesh	Algorithms
dc.subject.mesh	Cardiovascular Diseases
dc.subject.mesh	Male
dc.subject.mesh	Female
dc.subject.mesh	Middle Aged
dc.subject.mesh	Aged
dc.subject.mesh	Adult
dc.subject.mesh	Humans
dc.subject.mesh	Cardiovascular Diseases
dc.subject.mesh	Algorithms
dc.subject.mesh	Adult
dc.subject.mesh	Aged
dc.subject.mesh	Middle Aged
dc.subject.mesh	Female
dc.subject.mesh	Male
dc.subject.mesh	Humans
dc.subject.mesh	Algorithms
dc.subject.mesh	Cardiovascular Diseases
dc.subject.mesh	Male
dc.subject.mesh	Female
dc.subject.mesh	Middle Aged
dc.subject.mesh	Aged
dc.subject.mesh	Adult
dc.title	Revealing the nature of cardiovascular disease using DERGA, a novel data ensemble refinement greedy algorithm.
dc.type	Journal Article
utslib.citation.volume	412
utslib.location.activity	Netherlands
utslib.for	1102 Cardiorespiratory Medicine and Haematology
utslib.for	1117 Public Health and Health Services
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
utslib.copyright.status	closed_access	*
dc.date.updated	2025-01-15T05:42:23Z
pubs.publication-status	Published
pubs.volume	412

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.

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http://hdl.handle.net/10453/183636