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.
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.
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.
摘要：
背景：该研究旨在确定与CVD相关的最关键的参数，并采用新颖的数据集成细化程序来揭示这些参数的最佳模式，这可以导致高预测精度。
结果：总共收集了369名患者的数据，281名患有CVD或有发展风险的患者，与88个其他健康的人相比。在281名心血管疾病或高危患者中，53例被诊断为冠状动脉疾病（CAD），16患有终末期肾病，47例新诊断为2型糖尿病和92例慢性炎症性疾病（21类风湿性关节炎，41牛皮癣，30血管炎）。使用基于人工智能的算法分析数据，其主要目的是识别定义CVD的参数的最佳模式。该研究强调了使用DERGA和ExtraTrees算法识别心血管疾病可能性的六参数组合的有效性。这些参数,按重要性排序，包括血小板衍生的微囊泡（PMV），高血压,年龄,吸烟,血脂异常,身体质量指数（BMI）。内皮和红细胞MV，与糖尿病一起是最不重要的预测因素。此外,达到的最高预测精度为98.64%。值得注意的是,单独使用PMV可以获得91.32%的准确率，而采用所有十个参数的最优模型，得到的预测精度为0.9783(97.83%)。
结论：我们的研究显示了DERGA的疗效，一种创新的数据集成细化贪婪算法。DERGA加速评估个体发生CVD的风险，允许早期诊断，显著减少所需实验室测试的数量，并优化资源利用率。此外,它有助于确定对评估CVD敏感性至关重要的最佳参数，从而增强我们对潜在机制的理解。
结果：总共收集了369名患者的数据，281名患有CVD或有发展风险的患者，与88个其他健康的人相比。在281名心血管疾病或高危患者中，53例被诊断为冠状动脉疾病（CAD），16患有终末期肾病，47例新诊断为2型糖尿病和92例慢性炎症性疾病（21类风湿性关节炎，41牛皮癣，30血管炎）。使用基于人工智能的算法分析数据，其主要目的是识别定义CVD的参数的最佳模式。该研究强调了使用DERGA和ExtraTrees算法识别心血管疾病可能性的六参数组合的有效性。这些参数,按重要性排序，包括血小板衍生的微囊泡（PMV），高血压,年龄,吸烟,血脂异常,身体质量指数（BMI）。内皮和红细胞MV，与糖尿病一起是最不重要的预测因素。此外,达到的最高预测精度为98.64%。值得注意的是,单独使用PMV可以获得91.32%的准确率，而采用所有十个参数的最优模型，得到的预测精度为0.9783(97.83%)。
结论：我们的研究显示了DERGA的疗效，一种创新的数据集成细化贪婪算法。DERGA加速评估个体发生CVD的风险，允许早期诊断，显著减少所需实验室测试的数量，并优化资源利用率。此外,它有助于确定对评估CVD敏感性至关重要的最佳参数，从而增强我们对潜在机制的理解。
