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Highly cited papers are influenced by external factors that are not directlyrelated to the document's intrinsic quality. In this study, 50 characteristicsfor measuring the performance of 68 highly cited papers, from the Journal ofthe American Medical Informatics Association indexed in Web of Sciences (WoS),from 2009 to 2019 were investigated. In the first step, a Pearson correlationanalysis is performed to eliminate variables with zero or weak correlation withthe target (dependent) variable ([number of citations in WOS]). Consequently,32 variables are selected for the next step. By applying the Ridge technique,13 features show a positive effect on the number of citations. Using threedifferent algorithms, i.e., Ridge, Lasso, and Boruta, 6 factors appear to bethe most relevant ones. The [Number of citations by international researchers],[Journal self-citations in citing documents], and [Authors' self-citations inciting documents], are recognized as the most important features by all threemethods here used. The [First author's scientific age], [Open-access paper],and [Number of first author's citations in WOS] are identified as the importantfeatures of highly cited papers by only two methods, Ridge and Lasso. Noticethat we use specific machine learning algorithms as feature selection methods(Ridge, Lasso, and Boruta) to identify the most important features of highlycited papers, tools that had not previously been used for this purpose. Inconclusion, we re-emphasize the performance resulting from such algorithms.Moreover, we do not advise authors to seek to increase the citations of theirarticles by manipulating the identified performance features. Indeed, ethicalrules regarding these characteristics must be strictly obeyed.

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Identification of the most important external features of highly cited  scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection  data mining methods