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For: 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]
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48 Díaz-alcaide S, Martínez-santos P. Mapping fecal pollution in rural groundwater supplies by means of artificial intelligence classifiers. Journal of Hydrology 2019;577:124006. [DOI: 10.1016/j.jhydrol.2019.124006] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 3.0] [Reference Citation Analysis]
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61 Ijaz M, Alfian G, Syafrudin M, Rhee J. Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest. Applied Sciences 2018;8:1325. [DOI: 10.3390/app8081325] [Cited by in Crossref: 103] [Cited by in F6Publishing: 105] [Article Influence: 20.6] [Reference Citation Analysis]
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