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For: Sakr S, Elshawi R, Ahmed AM, Qureshi WT, Brawner CA, Keteyian SJ, Blaha MJ, Al-Mallah MH. Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project. BMC Med Inform Decis Mak 2017;17:174. [PMID: 29258510 DOI: 10.1186/s12911-017-0566-6] [Cited by in Crossref: 38] [Cited by in F6Publishing: 40] [Article Influence: 6.3] [Reference Citation Analysis]
Number Citing Articles
1 Sato H, Kimura Y, Ohba M, Ara Y, Wakabayashi S, Watanabe H. Prediction of Prednisolone Dose Correction Using Machine Learning. J Healthc Inform Res 2023;7:84-103. [PMID: 36910914 DOI: 10.1007/s41666-023-00128-3] [Reference Citation Analysis]
2 Li Z, Yang N, He L, Wang J, Ping F, Li W, Xu L, Zhang H, Li Y. Development and validation of questionnaire-based machine learning models for predicting all-cause mortality in a representative population of China. Front Public Health 2023;11:1033070. [PMID: 36778549 DOI: 10.3389/fpubh.2023.1033070] [Reference Citation Analysis]
3 Chua S, Sii CI, Ellyza Nohuddin PN. Comparative Analysis of Machine Learning Models for Fitness Level Prediction with Imbalanced Dataset. 2022 International Conference on Digital Transformation and Intelligence (ICDI) 2022. [DOI: 10.1109/icdi57181.2022.10007339] [Reference Citation Analysis]
4 Ebrahimi A, Wiil UK, Naemi A, Mansourvar M, Andersen K, Nielsen AS. Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods. BMC Med Inform Decis Mak 2022;22:304. [PMID: 36424597 DOI: 10.1186/s12911-022-02051-w] [Reference Citation Analysis]
5 Khan PW, Park S, Lee S, Byun Y, Miralinaghi M. Electric Kickboard Demand Prediction in Spatiotemporal Dimension Using Clustering-Aided Bagging Regressor. Journal of Advanced Transportation 2022;2022:1-15. [DOI: 10.1155/2022/8062932] [Reference Citation Analysis]
6 de Souza E Silva CG, Buginga GC, de Souza E Silva EA, Arena R, Rouleau CR, Aggarwal S, Wilton SB, Austford L, Hauer T, Myers J. Prediction of Mortality in Coronary Artery Disease: Role of Machine Learning and Maximal Exercise Capacity. Mayo Clin Proc 2022;97:1472-82. [PMID: 35431026 DOI: 10.1016/j.mayocp.2022.01.016] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
7 Daradkeh M. Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media: A Data Analytics Framework. International Journal of Business Analytics 2022;9:1-22. [DOI: 10.4018/ijban.292056] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
8 Vazquez-Zapien GJ, Mata-Miranda MM, Garibay-Gonzalez F, Sanchez-Brito M. Artificial intelligence model validation before its application in clinical diagnosis assistance. World J Gastroenterol 2022; 28(5): 602-604 [DOI: 10.3748/wjg.v28.i5.602] [Reference Citation Analysis]
9 Rathore N, Jain PK, Parida M. A Sustainable Model for Emergency Medical Services in Developing Countries: A Novel Approach Using Partial Outsourcing and Machine Learning. RMHP 2022;Volume 15:193-218. [DOI: 10.2147/rmhp.s338186] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Mattogno PP, Caccavella VM, Giordano M, D'Alessandris QG, Chiloiro S, Tariciotti L, Olivi A, Lauretti L. Interpretable Machine Learning-Based Prediction of Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Transsphenoidal Pituitary Surgery: A Pilot Study. J Neurol Surg B Skull Base 2022;83:485-95. [PMID: 36091632 DOI: 10.1055/s-0041-1740621] [Reference Citation Analysis]
11 Sulthana S, Reddy BNM. Deep Neural Networks and Black Widow Optimization for VANETS. Inventive Systems and Control 2022. [DOI: 10.1007/978-981-19-1012-8_48] [Reference Citation Analysis]
12 Abeba G, Alemneh E. Identification of Nonfunctional Requirement Conflicts: Machine Learning Approach. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2022. [DOI: 10.1007/978-3-030-93709-6_29] [Reference Citation Analysis]
13 Mazza M, Kammler-Sücker K, Leménager T, Kiefer F, Lenz B. Virtual reality: a powerful technology to provide novel insight into treatment mechanisms of addiction. Transl Psychiatry 2021;11:617. [PMID: 34873146 DOI: 10.1038/s41398-021-01739-3] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 4.0] [Reference Citation Analysis]
14 Mehedi IM, Shah MHM. Categorization of Webpages using dynamic mutation based differential evolution and gradient boost classifier. J Ambient Intell Human Comput. [DOI: 10.1007/s12652-021-03601-2] [Reference Citation Analysis]
15 Ugwoke PO, Bakpo FS, Udanor CN, Okoronkwo MC. A framework for monitoring movements of pandemic disease patients based on GPS trajectory datasets. Wireless Netw 2022;28:1-28. [DOI: 10.1007/s11276-021-02819-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
16 Della Pepa GM, Caccavella VM, Menna G, Ius T, Auricchio AM, Sabatino G, La Rocca G, Chiesa S, Gaudino S, Marchese E, Olivi A. Machine Learning-Based Prediction of Early Recurrence in Glioblastoma Patients: A Glance Towards Precision Medicine. Neurosurgery 2021;89:873-83. [PMID: 34459917 DOI: 10.1093/neuros/nyab320] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 Park HY, Jung H, Lee S, Kim JW, Cho HL, Nam SS. Estimated Artificial Neural Network Modeling of Maximal Oxygen Uptake Based on Multistage 10-m Shuttle Run Test in Healthy Adults. Int J Environ Res Public Health 2021;18:8510. [PMID: 34444259 DOI: 10.3390/ijerph18168510] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
18 Binkheder S, Aldekhyyel R, Almulhem J. Health informatics publication trends in Saudi Arabia: a bibliometric analysis over the last twenty-four years. J Med Libr Assoc 2021;109:219-39. [PMID: 34285665 DOI: 10.5195/jmla.2021.1072] [Reference Citation Analysis]
19 Kuo CC, Wang HH, Tseng LP. Using data mining technology to predict medication-taking behaviour in women with breast cancer: A retrospective study. Nurs Open 2021. [PMID: 34156764 DOI: 10.1002/nop2.963] [Reference Citation Analysis]
20 Guo C, Liu M, Lu M. A Dynamic Ensemble Learning Algorithm based on K-means for ICU mortality prediction. Applied Soft Computing 2021;103:107166. [DOI: 10.1016/j.asoc.2021.107166] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
21 Erkinay Ozdemir M, Ali Z, Subeshan B, Asmatulu E. Applying machine learning approach in recycling. J Mater Cycles Waste Manag 2021;23:855-71. [DOI: 10.1007/s10163-021-01182-y] [Cited by in Crossref: 12] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
22 Aromolaran O, Oyelade J, Adebiyi E. Performance evaluation of features for gene essentiality prediction. IOP Conf Ser : Earth Environ Sci 2021;655:012019. [DOI: 10.1088/1755-1315/655/1/012019] [Reference Citation Analysis]
23 Karaboga HA, Gunel A, Korkut SV, Demir I, Celik R. Bayesian Network as a Decision Tool for Predicting ALS Disease. Brain Sci 2021;11:150. [PMID: 33498784 DOI: 10.3390/brainsci11020150] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
24 Truong VT, Beyerbach D, Mazur W, Wigle M, Bateman E, Pallerla A, Ngo TNM, Shreenivas S, Tretter JT, Palmer C, Kereiakes DJ, Chung ES. Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement. Pacing Clin Electrophysiol 2021;44:334-40. [PMID: 33433905 DOI: 10.1111/pace.14163] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
25 Adeyinka DA, Muhajarine N. Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models. BMC Med Res Methodol 2020;20:292. [PMID: 33267817 DOI: 10.1186/s12874-020-01159-9] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 3.3] [Reference Citation Analysis]
26 Sahour H, Gholami V, Vazifedan M. A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer. Journal of Hydrology 2020;591:125321. [DOI: 10.1016/j.jhydrol.2020.125321] [Cited by in Crossref: 44] [Cited by in F6Publishing: 37] [Article Influence: 14.7] [Reference Citation Analysis]
27 Elshawi R, Sherif Y, Al‐mallah M, Sakr S. Interpretability in healthcare: A comparative study of local machine learning interpretability techniques. Computational Intelligence 2021;37:1633-50. [DOI: 10.1111/coin.12410] [Cited by in Crossref: 17] [Cited by in F6Publishing: 21] [Article Influence: 5.7] [Reference Citation Analysis]
28 Smith BP, Auvil LS, Welge M, Bushell CB, Bhargava R, Elango N, Johnson K, Madak-Erdogan Z. Identification of early liver toxicity gene biomarkers using comparative supervised machine learning. Sci Rep 2020;10:19128. [PMID: 33154507 DOI: 10.1038/s41598-020-76129-8] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
29 Ding X, Li Y, Li D, Li L, Liu X. Using machine-learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment. Brain Behav 2020;10:e01814. [PMID: 32862513 DOI: 10.1002/brb3.1814] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 4.3] [Reference Citation Analysis]
30 Hong WH, Yap JH, Selvachandran G, Thong PH, Son LH. Forecasting mortality rates using hybrid Lee–Carter model, artificial neural network and random forest. Complex Intell Syst 2021;7:163-89. [DOI: 10.1007/s40747-020-00185-w] [Cited by in Crossref: 10] [Cited by in F6Publishing: 12] [Article Influence: 3.3] [Reference Citation Analysis]
31 Berto TM, Santos MC, Pereira FMV, Filletti ÉR. Artificial neural networks applied to the classification of hair samples according to pigment and sex using non‐invasive analytical techniques. X‐Ray Spectrom 2020;49:632-41. [DOI: 10.1002/xrs.3163] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
32 Gravesteijn BY, Nieboer D, Ercole A, Lingsma HF, Nelson D, van Calster B, Steyerberg EW; CENTER-TBI collaborators. Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury. J Clin Epidemiol 2020;122:95-107. [PMID: 32201256 DOI: 10.1016/j.jclinepi.2020.03.005] [Cited by in Crossref: 67] [Cited by in F6Publishing: 72] [Article Influence: 22.3] [Reference Citation Analysis]
33 Guo C, Lu M, Chen J. An evaluation of time series summary statistics as features for clinical prediction tasks. BMC Med Inform Decis Mak 2020;20:48. [PMID: 32138733 DOI: 10.1186/s12911-020-1063-x] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
34 Olawade OE, Onashoga SA, Arogundade O'. Comparative Analysis of Machine Learning Techniques in Health System. 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS) 2020. [DOI: 10.1109/icmcecs47690.2020.240861] [Reference Citation Analysis]
35 Wang J, Deng F, Zeng F, Shanahan AJ, Li WV, Zhang L. Predicting long-term multicategory cause of death in patients with prostate cancer: random forest versus multinomial model.. [DOI: 10.1101/2020.01.03.893966] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
36 A REVIEW ON MACHINE LEARNING TECHNIQUES FOR ADVANCED HEALTH CARE SYSTEMS. IJESRT 2020;9:1-7. [DOI: 10.29121/ijesrt.v9.i11.2020.1] [Reference Citation Analysis]
37 Ullah R, Khan S, Ali H, Chaudhary II, Bilal M, Ahmad I. A comparative study of machine learning classifiers for risk prediction of asthma disease. Photodiagnosis Photodyn Ther 2019;28:292-6. [PMID: 31614223 DOI: 10.1016/j.pdpdt.2019.10.011] [Cited by in Crossref: 18] [Cited by in F6Publishing: 12] [Article Influence: 4.5] [Reference Citation Analysis]
38 Watomakin DB, Emanuel AWR. Comparison of Performance Support Vector Machine Algorithm and Naive Bayes for Diabetes Diagnosis. 2019 5th International Conference on Science in Information Technology (ICSITech) 2019. [DOI: 10.1109/icsitech46713.2019.8987464] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
39 [DOI: 10.1109/cbms.2019.00065] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 2.5] [Reference Citation Analysis]
40 Boratto L, Carta S, Ibba F, Mulas F, Pilloni P. Modeling real-time data and contextual information from workouts in eCoaching platforms to predict users’ sharing behavior on Facebook. User Model User-Adap Inter 2020;30:395-411. [DOI: 10.1007/s11257-019-09229-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
41 Lee S, Choe EK, Park B. Exploration of Machine Learning for Hyperuricemia Prediction Models Based on Basic Health Checkup Tests. J Clin Med 2019;8:E172. [PMID: 30717373 DOI: 10.3390/jcm8020172] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 2.8] [Reference Citation Analysis]
42 Elshawi R, Sherif Y, Al-mallah M, Sakr S. ILIME: Local and Global Interpretable Model-Agnostic Explainer of Black-Box Decision. Advances in Databases and Information Systems 2019. [DOI: 10.1007/978-3-030-28730-6_4] [Cited by in Crossref: 3] [Article Influence: 0.8] [Reference Citation Analysis]
43 Sakr S, Elshawi R, Ahmed A, Qureshi WT, Brawner C, Keteyian S, Blaha MJ, Al-Mallah MH. Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project. PLoS One 2018;13:e0195344. [PMID: 29668729 DOI: 10.1371/journal.pone.0195344] [Cited by in Crossref: 55] [Cited by in F6Publishing: 59] [Article Influence: 11.0] [Reference Citation Analysis]