000161985 001__ 161985
000161985 005__ 20251017144610.0
000161985 0247_ $$2doi$$a10.3390/technologies13060222
000161985 0248_ $$2sideral$$a144609
000161985 037__ $$aART-2025-144609
000161985 041__ $$aeng
000161985 100__ $$aOlivera Solís, Rafael Alejandro
000161985 245__ $$aA Novel Dataset for Early Cardiovascular Risk Detection in School Children Using Machine Learning
000161985 260__ $$c2025
000161985 5060_ $$aAccess copy available to the general public$$fUnrestricted
000161985 5203_ $$aThis study introduces the PROCDEC dataset, a novel collection of 1140 cases with 30 cardiovascular risk factors gathered over a 10-year period from school children in Santa Clara, Cuba. The dataset was curated with input from medical experts in pediatric cardiology, endocrinology, general medicine, and clinical laboratory, ensuring its clinical relevance. We conducted a rigorous performance evaluation of 10 machine learning (ML) algorithms to classify cardiovascular risk into two categories: at risk and not at risk. The models were assessed using a stratified k-fold cross-validation approach to enhance the reliability of the findings. Among the evaluated models—Bayes Net, Naive Bayes, SMO, K-Nearest Neighbors (KNN), Logistic Regression, AdaBoost, Multilayer Perceptron (MLP), J48, Logistic Model Tree (LMT), and Random Forest (RF)—the best-performing classifiers (MLP, LMT, J48 and Logistic Regression) achieved F1-score values exceeding 0.83, indicating strong predictive capability. To improve interpretability, we employed feature selection techniques to rank the most influential risk factors. Key contributors to classification performance included hypertension, hyperreactivity, body mass index (BMI), uric acid, cholesterol, parental hypertension, and sibling dyslipidemia. These findings align with established clinical knowledge and reinforce the potential of ML models for pediatric cardiovascular risk assessment. Unlike previous studies, our research not only evaluates multiple ML techniques but also emphasizes their clinical applicability and interpretability, which are critical for real-world implementation. Future work will focus on validating these models with external datasets and integrating them into decision-support systems for early risk detection.
000161985 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T31-20R$$9info:eu-repo/grantAgreement/ES/MCINN/PID2022-136476OB-I00
000161985 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000161985 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000161985 700__ $$aGonzález Rodríguez, Emilio Francisco
000161985 700__ $$aCastañeda Sheissa, Roberto
000161985 700__ $$aLorenzo-Ginori, Juan Valentín
000161985 700__ $$0(orcid)0000-0001-9485-7678$$aGarcía, José$$uUniversidad de Zaragoza
000161985 7102_ $$15008$$2560$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Ingeniería Telemática
000161985 773__ $$g13, 6 (2025), 222 [18 pp.]$$pTechnologies$$tTECHNOLOGIES$$x2227-7080
000161985 8564_ $$s477324$$uhttps://zaguan.unizar.es/record/161985/files/texto_completo.pdf$$yVersión publicada
000161985 8564_ $$s2647047$$uhttps://zaguan.unizar.es/record/161985/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000161985 909CO $$ooai:zaguan.unizar.es:161985$$particulos$$pdriver
000161985 951__ $$a2025-10-17-14:16:52
000161985 980__ $$aARTICLE