{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:21:03Z","timestamp":1779254463224,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T00:00:00Z","timestamp":1743724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Cardiovascular diseases (CVDs) are among the leading causes of mortality worldwide, particularly in Mexico, where rural regions face challenges due to limited access to medical equipment. This preliminary study proposes a low-cost cardiovascular disease classifier, Buazduino-001, which integrates machine learning (ML) techniques with Arduino-based technology to provide accessible and non-invasive risk assessment. Three classical ML models\u2014logistic regression, random forest, and support vector machine\u2014were implemented and evaluated using a dataset of 303 patients from the UCI Machine Learning Repository. This study introduces a six-stage methodology, including a novel step that prioritizes non-invasive attributes to optimize diagnostic time and cost. The random forest model demonstrated the best performance, achieving 87% classification accuracy, with a reduced feature set of five attributes (sex, age, chest pain, heart rate, and exercise-induced angina). In this preliminary study, the system was validated experimentally with 30 patients, confirming an 85% accuracy and an 80% reduction in diagnostic time compared to traditional medical assessments. The results highlight the practicality of combining ML with low-cost electronics to address healthcare gaps in resource-limited settings. While this study is preliminary, the Buazduino-001 system demonstrates potential for early CVD risk detection and could serve as a screening tool in rural clinics, complementing conventional diagnostic methods.<\/jats:p>","DOI":"10.3390\/a18040202","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T03:36:45Z","timestamp":1743737805000},"page":"202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Preliminary Study on Machine Learning Techniques to Classify Cardiovascular Diseases in Mexico"],"prefix":"10.3390","volume":"18","author":[{"given":"Claudia Sifuentes","family":"Gallardo","sequence":"first","affiliation":[{"name":"Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Zacatecas 98160, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5245-0156","authenticated-orcid":false,"given":"Misael Zambrano","family":"de la Torre","sequence":"additional","affiliation":[{"name":"Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Zacatecas 98160, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel Alaniz","family":"Lumbreras","sequence":"additional","affiliation":[{"name":"Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Zacatecas 98160, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Efren","family":"Gonzalez-Ramirez","sequence":"additional","affiliation":[{"name":"Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Zacatecas 98160, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7337-8974","authenticated-orcid":false,"given":"Jos\u00e9 Ismael De la Rosa","family":"Vargas","sequence":"additional","affiliation":[{"name":"Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Zacatecas 98160, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1519-7718","authenticated-orcid":false,"given":"Carlos","family":"Olvera-Olvera","sequence":"additional","affiliation":[{"name":"Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Zacatecas 98160, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0729-0541","authenticated-orcid":false,"given":"Jos\u00e9 Ortega","family":"Sigala","sequence":"additional","affiliation":[{"name":"Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Zacatecas 98160, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar Alejandro","family":"Guirette-Barbosa","sequence":"additional","affiliation":[{"name":"Carrera de Ingenier\u00eda Industrial, Universidad Polit\u00e9cnica de Zacatecas, Plan de Pardillo Sn, Fresnillo 99056, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1320-4371","authenticated-orcid":false,"given":"Oscar Cruz","family":"Dom\u00ednguez","sequence":"additional","affiliation":[{"name":"Carrera de Ingenier\u00eda Industrial, Universidad Polit\u00e9cnica de Zacatecas, Plan de Pardillo Sn, Fresnillo 99056, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7190-3528","authenticated-orcid":false,"given":"H\u00e9ctor Dur\u00e1n","family":"Mu\u00f1oz","sequence":"additional","affiliation":[{"name":"Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Zacatecas 98160, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Development and validation of analytical methodology for quantification of propranolol hydrochloride in a multiparticulate biphasic system by uv-vis spectrophotometry","volume":"40","author":"Moherdaui","year":"2018","journal-title":"Acta Sci. 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