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The study designs and compares multiple algorithms, selecting Random Forest as the best-performing model with 90.43% accuracy. The model is integrated into a web platform using the PIMA dataset and validated by specialists from the Peruvian League for the Fight against Diabetes. Results show major reductions in information collection and diagnosis time, lower diagnosis cost, and improved usability, confirming significant process optimization.","See discussions, stats, and author profiles for this publication at: [https://www. researchgate. net/publication/373258552](https://www. researchgate. net/publication/373258552)  \nPredictive machine learning applying cross industry standard process for data mining for the diagnosis of diabetes mellitus type 2  \nArticle · December 2023 DOI: 10.11591/ijai.v12.i4.pp1713-1726  \nCITATIONS 0  \nREADS 148  \n8 authors, including:  \nFernando Alex Sierra-Liñan  \nUniversidad Privada del Norte (Perú)  \n24 PUBLICATIONS 54 CITATIONS  \nMichael Alejandro Cabanillas-Carbonell Universidad Privada del Norte (Perú)  \n90 PUBLICATIONS 155 CITATIONS  \nAll content following this page was uploaded by Fernando Alex Sierra-Liñan on 21 August 2023. The user has requested enhancement of the downloaded file.  \nPredictive machine learning applying cross industry standard process for data mining for the diagnosis of diabetes mellitus  \ntype 2  \nVictor Garcia-Rios1, Marieta Marres-Salhuana1, Fernando Sierra-Liñan2, Michael Cabanillas-Carbonell3  \n1Facultad de Ingeniería y Arquitectura, Universidad Autónoma del Perú, Lima, Perú  \n2Facultad de Ingeniería, Universidad Privada del Norte, Lima, Perú  \n3Vicerrectorado de Investigación, Universidad Privada Norbert Wiener, Lima, Perú  \nArticle history:  \nReceived Jul 15, 2022 Revised Jan 20, 2023 Accepted Jan 30, 2023  \nKeywords:  \nDiagnosis Machine learning Prediction Random forest  \nType 2 diabetes mellitus  \nCorresponding Author:  \nCurrently, type 2 diabetes mellitus is one of the world's most prevalent diseases and has claimed millions of people's lives. The present research aims to know the impact of the use of machine learning in the diagnostic process of type 2 diabetes mellitus and to offer a tool that facilitates the diagnosis of the dis-ease quickly and easily. Different machine learning models were designed and compared, being random forest was the algorithm that generated the model with the best performance (90 .43% accuracy), which was integrated into a web platform, working with the PIMA dataset, which was validated by specialists from the Peruvian League for the Fight against Diabetes organization. The result was a decrease of (A) 88.28% in the information collection time,(B) 99 .99% in the diagnosis time,(C) 44 .42% in the diagnosis cost, and (D) 100% in the level of difficulty, concluding that the application of machine learning can significantly optimize the diagnostic process of type 2 diabetes mellitus.  \nThis is an open access article under the CC BY-SA license.  \nMichael Cabanillas-Carbonell Universidad Privada Norbert Wiener Lima, Perú [Email: mcabanillas@ieee.org](Email: mcabanillas@ieee.org)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn recent years, diabetes mellitus has increased its prevalence on the world scene, information from the World Diabetes Atlas, which is periodically published by the International Diabetes Federation, shows that by the year 2045 diabetes mellitus is projected to increase by up to 143% on the African continent and 55% in South America [1] . In addition, the publication mentions that there are currently approximately 537 million people around the world who suffer from diabetes, of which 352 million are in the active phase, within a range of 20 to 64 years of age. The problematic situation of the study is based on the increasing prevalence of type 2 diabetes mellitus in the world scenario, at present, due to COVID-19, people suffering from it are the most likely to develop a critical picture of this disease, which is why early diagnosis is so important [2] . Type 2 diabetes is caused by varying degrees of insulin resistance, altered insulin secretion, increased glucose production, and various genetic metabolic defects in insulin action [3] . According to the pan american health organization (PAHO) [4], it has been identified that approximately 90% to 95% of all cases suffer from type 2 diabetes [5], i.e., of the three main types of diabetes mellitus, type 2 is the m","cbCaij5I8lRkKnEa","https://ap.wps.com/l/cbCaij5I8lRkKnEa","pdf",1263277,1,15,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"What is the main goal of the research on type 2 diabetes diagnosis?\",\"answer\":\"To evaluate how predictive machine learning, using the CRISP-DM methodology, impacts the diagnostic process for type 2 diabetes mellitus and to provide a tool that enables faster, easier diagnosis.\"},{\"question\":\"Which machine learning model achieved the best performance?\",\"answer\":\"Random Forest produced the best model performance, reaching 90.43% accuracy.\"},{\"question\":\"How was the best-performing model deployed and validated?\",\"answer\":\"The Random Forest model was integrated into a web platform and used with the PIMA dataset, then validated by specialists from the Peruvian League for the Fight against Diabetes organization.\"}]","Predictive machine learning applying cross-industry standard process for data mining for the diagnosis of diabetes mellitus type 2 | 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