These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
175 related articles for article (PubMed ID: 33155096)
1. Feature selection and risk prediction for patients with coronary artery disease using data mining. Md Idris N; Chiam YK; Varathan KD; Wan Ahmad WA; Chee KH; Liew YM Med Biol Eng Comput; 2020 Dec; 58(12):3123-3140. PubMed ID: 33155096 [TBL] [Abstract][Full Text] [Related]
2. Diagnosing Coronary Artery Disease on the Basis of Hard Ensemble Voting Optimization. Mohammedqasim H; Mohammedqasem R; Ata O; Alyasin EI Medicina (Kaunas); 2022 Nov; 58(12):. PubMed ID: 36556946 [TBL] [Abstract][Full Text] [Related]
3. Development of an efficient novel method for coronary artery disease prediction using machine learning and deep learning techniques. Mansoor CMM; Chettri SK; Naleer HMM Technol Health Care; 2024; 32(6):4545-4569. PubMed ID: 39031414 [TBL] [Abstract][Full Text] [Related]
4. Predicting coronary artery disease: a comparison between two data mining algorithms. Ayatollahi H; Gholamhosseini L; Salehi M BMC Public Health; 2019 Apr; 19(1):448. PubMed ID: 31035958 [TBL] [Abstract][Full Text] [Related]
5. A new machine learning technique for an accurate diagnosis of coronary artery disease. Abdar M; Książek W; Acharya UR; Tan RS; Makarenkov V; Pławiak P Comput Methods Programs Biomed; 2019 Oct; 179():104992. PubMed ID: 31443858 [TBL] [Abstract][Full Text] [Related]
6. Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset. Velusamy D; Ramasamy K Comput Methods Programs Biomed; 2021 Jan; 198():105770. PubMed ID: 33027698 [TBL] [Abstract][Full Text] [Related]
8. Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project. Sakr S; Elshawi R; Ahmed AM; Qureshi WT; Brawner CA; Keteyian SJ; Blaha MJ; Al-Mallah MH BMC Med Inform Decis Mak; 2017 Dec; 17(1):174. PubMed ID: 29258510 [TBL] [Abstract][Full Text] [Related]
9. A database for using machine learning and data mining techniques for coronary artery disease diagnosis. Alizadehsani R; Roshanzamir M; Abdar M; Beykikhoshk A; Khosravi A; Panahiazar M; Koohestani A; Khozeimeh F; Nahavandi S; Sarrafzadegan N Sci Data; 2019 Oct; 6(1):227. PubMed ID: 31645559 [TBL] [Abstract][Full Text] [Related]
10. MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records. Huang Z; Chan TM; Dong W J Biomed Inform; 2017 Feb; 66():161-170. PubMed ID: 28065840 [TBL] [Abstract][Full Text] [Related]
11. Machine learning for prediction of 30-day mortality after ST elevation myocardial infraction: An Acute Coronary Syndrome Israeli Survey data mining study. Shouval R; Hadanny A; Shlomo N; Iakobishvili Z; Unger R; Zahger D; Alcalai R; Atar S; Gottlieb S; Matetzky S; Goldenberg I; Beigel R Int J Cardiol; 2017 Nov; 246():7-13. PubMed ID: 28867023 [TBL] [Abstract][Full Text] [Related]
12. Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models. Trigka M; Dritsas E Sensors (Basel); 2023 Jan; 23(3):. PubMed ID: 36772237 [TBL] [Abstract][Full Text] [Related]
13. Drug-Protein Interactions Prediction Models Using Feature Selection and Classification Techniques. Idhaya T; Suruliandi A; Raja SP Curr Drug Metab; 2023; 24(12):817-834. PubMed ID: 38270152 [TBL] [Abstract][Full Text] [Related]
14. A Machine Learning Model for Detection of Coronary Artery Disease Using Noninvasive Clinical Parameters. Sayadi M; Varadarajan V; Sadoughi F; Chopannejad S; Langarizadeh M Life (Basel); 2022 Nov; 12(11):. PubMed ID: 36431068 [No Abstract] [Full Text] [Related]
15. Using machine learning-based algorithms to construct cardiovascular risk prediction models for Taiwanese adults based on traditional and novel risk factors. Cheng CH; Lee BJ; Nfor ON; Hsiao CH; Huang YC; Liaw YP BMC Med Inform Decis Mak; 2024 Jul; 24(1):199. PubMed ID: 39039467 [TBL] [Abstract][Full Text] [Related]
16. Probabilistic Graphical Modeling for Estimating Risk of Coronary Artery Disease: Applications of a Flexible Machine-Learning Method. Gupta A; Slater JJ; Boyne D; Mitsakakis N; Béliveau A; Druzdzel MJ; Brenner DR; Hussain S; Arora P Med Decis Making; 2019 Nov; 39(8):1032-1044. PubMed ID: 31619130 [No Abstract] [Full Text] [Related]
17. Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: Comparison with traditional risk prediction approaches. Han D; Kolli KK; Gransar H; Lee JH; Choi SY; Chun EJ; Han HW; Park SH; Sung J; Jung HO; Min JK; Chang HJ J Cardiovasc Comput Tomogr; 2020; 14(2):168-176. PubMed ID: 31570323 [TBL] [Abstract][Full Text] [Related]
18. A Machine Learning Approach for the Prediction of the Progression of Cardiovascular Disease based on Clinical and Non-Invasive Imaging Data. Kigka VI; Georga EI; Sakellarios AI; Tachos NS; Andrikos I; Tsompou P; Rocchiccioli S; Pelosi G; Parodi O; Michalis LK; Fotiadis DI Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():6108-6111. PubMed ID: 30441728 [TBL] [Abstract][Full Text] [Related]
19. The Impact of Oversampling with SMOTE on the Performance of 3 Classifiers in Prediction of Type 2 Diabetes. Ramezankhani A; Pournik O; Shahrabi J; Azizi F; Hadaegh F; Khalili D Med Decis Making; 2016 Jan; 36(1):137-44. PubMed ID: 25449060 [TBL] [Abstract][Full Text] [Related]
20. Predicting the Cochlear Dead Regions Using a Machine Learning-Based Approach with Oversampling Techniques. Chang YS; Park HS; Moon IJ Medicina (Kaunas); 2021 Nov; 57(11):. PubMed ID: 34833410 [No Abstract] [Full Text] [Related] [Next] [New Search]