Helen Durgante:<br />
Just a gentle reminder of our request for your review of the submission, &quot;The Machine Learning and Behavioral Traits in Adolescents: A Systematic Review.,&quot; for Revista Interamericana de Psicología/Interamerican Journal of Psychology. We were hoping to have your response by 2021-02-13, and this email has been automatically generated and sent with the passing of that date.
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I believe that you would serve as an excellent reviewer of the manuscript. The submission's abstract is inserted below, and I hope that you will consider undertaking this important task for us.<br />
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Please log into the journal web site to indicate whether you will undertake the review or not, as well as to access the submission and to record your review and recommendation.<br />
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The review itself is due 2021-02-27.<br />
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Submission URL: <a href='https://journal.sipsych.org/index.php/IJP/reviewer/submission?submissionId=1362&reviewId=963&key=TUx6uh' class='submissionReviewUrl-style-class'>https://journal.sipsych.org/index.php/IJP/reviewer/submission?submissionId=1362&reviewId=963&key=TUx6uh</a><br />
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Thank you for considering this request.<br />
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Fernando Andres Polanco
Revista Interamericana de Psicología/Interamerican Journal of Psychology<br />
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&quot;The Machine Learning and Behavioral Traits in Adolescents: A Systematic Review.&quot;<br />
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<p><strong>Abstract </strong></p>
<p>During the last decade there has been an exponential growth of artificial intelligence models applied to the different fields of knowledge including psychology. Various studies have used these models in order to identify potential risks early. However, few studies focused on adolescents have used these techniques. This systematic review used the steps suggested by the Prisma model to identify studies that applied Machine Learning techniques to identify behavioral traits in adolescents. When applying the inclusion and exclusion criteria, 5 studies were identified in the PsycNET, PubMed, Scopus, Scielo, Web of Science and Science Direct databases. The main results show that the Machine learning algorithms mainly used individually or in combination, were <em>logistic regression</em> (n = 4) and <em>Support Vector Machine SVM</em> (n = 3) in addition to others such as <em>Adaboost</em> (n = 1) <em>Nested ten-fold crossvalidation</em> (n = 1), <em>Random Forest</em> (n = 1), <em>Artificial Neural Network AN</em>N (n = 1), and <em>Extreme gradient boosting XGB</em> (n = 1). This review highlights that the use of Machine learning methods provide reliable predictive tools as much or even more than the traditional statistical methods. Finally, the present review highlights the lack of studies using these tools in the field of psychology, mainly in adolescents.</p><br/><p>The following message is being delivered on behalf of Revista Interamericana de Psicología/Interamerican Journal of Psychology. ______________________________________</p>
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<div><strong><span class="size">Dr. Fernando Andres Polanco</span></strong></div>
<div><em>Editor</em></div>
<div><a href="mailto:fernando.polanco@sipsych.org" target="_blank" rel="noopener">fernando.polanco@sipsych.org</a>&nbsp;</div>
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<div>Sociedad Interamericana de Psicología</div>
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