Analyzing predictors of in-hospital mortality in patients with acute ST-segment elevation myocardial infarction using an evolved machine learning approach

支持向量机 机器学习 人工智能 心肌梗塞 特征选择 渡线 计算机科学 医学 内科学
作者
Mengge Gong,Dongjie Liang,Diyun Xu,Youkai Jin,Guoqing Wang,Peiren Shan
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:170: 107950-107950 被引量:8
标识
DOI:10.1016/j.compbiomed.2024.107950
摘要

Acute ST-segment elevation myocardial infarction (STEMI) is a severe cardiac ailment characterized by the sudden complete blockage of a portion of the coronary artery, leading to the interruption of blood supply to the myocardium. This study examines the medical records of 3205 STEMI patients admitted to the coronary care unit of the First Affiliated Hospital of Wenzhou Medical University from January 2014 to December 2021. In this research, a novel predictive framework for STEMI is proposed, incorporating evolutionary computational methods and machine learning techniques. A variant algorithm, AGCOSCA, is introduced by integrating crossover operation and observation bee strategy into the original Sine Cosine Algorithm (SCA). The effectiveness of AGCOSCA is initially validated using IEEE CEC 2017 benchmark functions, demonstrating its ability to mitigate the deficiency in local mining after SCA random perturbation. Building upon this foundation, the AGCOSCA approach has been paired with Support Vector Machine (SVM) to forge the predictive framework referred to as AGCOSCA-SVM. Specifically, AGCOSCA is employed to refine the selection of predictors from a substantial feature set before SVM is utilized to forecast the occurrence of STEMI. In our analysis, we observed that SVM excels at managing nonlinear data relationships, a strength that becomes particularly prominent in smaller datasets of STEMI patients. To assess the effectiveness of AGCOSCA-SVM, diagnostic experiments were conducted based on the STEMI sample data. Results indicate that AGCOSCA-SVM outperforms traditional machine learning methods, achieving superior Accuracy, Sensitivity, and Specificity values of 97.83 %, 93.75 %, and 96.67 %, respectively. The selected features, such as acute kidney injury (AKI) stage, fibrinogen, mean platelet volume (MPV), free triiodothyronine (FT3), diuretics, and Killip class during hospitalization, are identified as crucial for predicting STEMI. In conclusion, AGCOSCA-SVM emerges as a promising model framework for supporting the diagnostic process of STEMI, showcasing potential applications in clinical settings.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
123完成签到 ,获得积分10
刚刚
所所应助JSY采纳,获得30
刚刚
默默的立辉完成签到,获得积分10
刚刚
Yh完成签到,获得积分10
刚刚
引子完成签到,获得积分10
2秒前
机智的阿振完成签到,获得积分10
3秒前
KatzeBaliey完成签到,获得积分10
4秒前
量子星尘发布了新的文献求助10
5秒前
yar应助大饼采纳,获得10
6秒前
mammer应助一朵云采纳,获得20
6秒前
6秒前
Jason完成签到,获得积分10
7秒前
害羞凤灵完成签到,获得积分10
7秒前
芳芳完成签到,获得积分10
8秒前
风起枫落完成签到 ,获得积分10
8秒前
xkhxh完成签到 ,获得积分10
9秒前
zzq778发布了新的文献求助10
9秒前
小马甲应助双儿采纳,获得10
10秒前
江南烟雨如笙完成签到 ,获得积分10
11秒前
王洋应助枕星采纳,获得10
14秒前
笨笨寒天完成签到,获得积分10
14秒前
Hello应助zzq778采纳,获得10
14秒前
15秒前
铜豌豆完成签到 ,获得积分10
15秒前
稞小弟完成签到,获得积分10
15秒前
16秒前
18秒前
18秒前
zzzz发布了新的文献求助10
19秒前
小马完成签到,获得积分10
21秒前
21秒前
一朵云完成签到,获得积分10
22秒前
JSY发布了新的文献求助30
23秒前
浩铭完成签到,获得积分10
24秒前
Iven发布了新的文献求助10
24秒前
26秒前
28秒前
冷酷的天晴完成签到,获得积分10
28秒前
ysy完成签到,获得积分10
29秒前
29秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038368
求助须知:如何正确求助?哪些是违规求助? 3576068
关于积分的说明 11374313
捐赠科研通 3305780
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029