https://ojs.imeti.org/index.php/EMSI <p><em><strong>Emerging Science Innovation</strong></em> (EMSI) is an international, multidiscipline, peer-reviewed scholarly journal. It is dedicated to providing a platform for fast communication between emerging studies on Science innovations. A paper will be online shortly once it is accepted and typeset. Currently, there is no publication charge, including article processing and submission charges. EMSI is an open access journal which means that all contents are freely available without charge to the user or his/her institution. The ISSN Number of EMSI will be applied after the first Issue has been published.</p> <p><img src="http://ojs.imeti.org/public/site/images/ijeti/google6.png"> <img src="https://ojs.imeti.org/public/site/images/allen/crossref3.png"></p> Taiwan Association of Engineering and Technology Innovation en-US Emerging Science Innovation https://ojs.imeti.org/index.php/EMSI/article/view/11285 <p>This paper aims to review the performance of different machine learning (ML) models and develop models for the automated diagnosis of chronic kidney disease. To detect chronic kidney disease with better precision, selecting the right and better-performing ML model is significant as it improves the precision and accuracy of the chronic kidney disease diagnosis. The study uses the Joana Briggs Institute (JBI) scoping review methodology, which involves different steps such as searching relevant literature, conducting the review, and reporting the review result. In the search, the year of publication and the indexing of journals where the studies are published is used as inclusion and exclusion criteria. The review result shows that the current chronic kidney disease detection has focused on the development of ensemble-based and deep-learning methods. The deep learning method can achieve a higher accuracy of 99.75%.</p> Tsehay Admassu Assegie Yenework Belayneh Chekol Copyright (c) 2023 Emerging Science Innovation 2023-02-08 2023-02-08 10.46604/emsi.2023.11285