{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:18:58Z","timestamp":1760059138521,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Cardiovascular diseases stand as the leading cause of mortality worldwide, underscoring the urgent need for effective tools that enable early detection and monitoring of at-risk patients. This study combines Artificial Intelligence (AI) techniques\u2014specifically the k-means clustering algorithm\u2014alongside dimensionality reduction methods like Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) to identify patient groups with varying levels of heart attack risk. We used a publicly available clinical dataset with 1319 patient records, which included variables such as age, gender, blood pressure, glucose levels, CK-MB Creatine Kinase MB (KCM), and troponin levels. We normalized and prepared the data, then we employed PCA and UMAP to reduce dimensionality and facilitate visualization. Using the k-means algorithm, we segmented the patients into distinct groups based on their clinical features. Our analysis revealed two distinct patient groups. Group 2 exhibited significantly higher levels of troponin (mean 0.4761 ng\/mL), KCM (18.65 ng\/mL), and glucose (mean 148.19 mg\/dL) and was predominantly composed of men (97%). These factors indicate an increased risk of cardiac events compared to Group 1, which had lower levels of these biomarkers and a slightly higher average age. Interestingly, no significant differences in blood pressure were observed between the groups. This study demonstrates the effectiveness of combining Machine Learning (ML) techniques with dimensionality reduction methods to enhance risk stratification accuracy in cardiology. By enabling more targeted interventions for high-risk patients, our unsupervised segmentation approach focuses on intrinsic data patterns rather than predefined diagnostic labels, serves as a powerful complement to traditional risk assessment tools.<\/jats:p>","DOI":"10.3390\/make7020046","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T07:04:28Z","timestamp":1747897468000},"page":"46","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0770-6869","authenticated-orcid":false,"given":"Joan D.","family":"Gonzalez-Franco","sequence":"first","affiliation":[{"name":"Department of Electronics and Telecommunications, CICESE Research Center, Carretera Ensenada-Tijuana 3918, Playitas, Ensenada 22860, BC, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alejandro","family":"Galaviz-Mosqueda","sequence":"additional","affiliation":[{"name":"Monterrey CICESE Research Center, Alianza Centro 504, Apodaca 66629, NL, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7219-361X","authenticated-orcid":false,"given":"Salvador","family":"Villarreal-Reyes","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunications, CICESE Research Center, Carretera Ensenada-Tijuana 3918, Playitas, Ensenada 22860, BC, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6154-5712","authenticated-orcid":false,"given":"Jose E.","family":"Lozano-Rizk","sequence":"additional","affiliation":[{"name":"Division of Telematics, CICESE Research Center, Carretera Ensenada-Tijuana 3918, Playitas, Ensenada 22860, BC, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1968-8525","authenticated-orcid":false,"given":"Raul","family":"Rivera-Rodriguez","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunications, CICESE Research Center, Carretera Ensenada-Tijuana 3918, Playitas, Ensenada 22860, BC, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2018-1833","authenticated-orcid":false,"given":"Jose E.","family":"Gonzalez-Trejo","sequence":"additional","affiliation":[{"name":"Division of Telematics, CICESE Research Center, Carretera Ensenada-Tijuana 3918, Playitas, Ensenada 22860, BC, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4022-7405","authenticated-orcid":false,"given":"Alexei-Fedorovish","family":"Licea-Navarro","sequence":"additional","affiliation":[{"name":"Department of Biomedical Innovation, CICESE Research Center, Carretera Ensenada-Tijuana 3918, Playitas, Ensenada 22860, BC, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge","family":"Lozoya-Arandia","sequence":"additional","affiliation":[{"name":"Department of Data Science, CUChapala, Universidad de Guadalajara, Av. Ju\u00e1rez 976, Col. Americana, Americana, Guadalajara 44100, JA, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edgar A.","family":"Ibarra-Flores","sequence":"additional","affiliation":[{"name":"Head of Education and Research, Ensenada ISSSTE Hospital Clinic, Calle Delante, Militar, Ensenada 22890, BC, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,22]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO) (2024, October 23). Cardiovascular Diseases (CVDs). Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-(cvds)."},{"key":"ref_2","first-page":"1348795","article-title":"Rhodiola rosea: A Therapeutic Candidate on Cardiovascular Diseases","volume":"2022","author":"Chen","year":"2022","journal-title":"Oxidative Med. 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