TIMELY DIAGNOSIS OF METABOLIC SYNDROME USING THE SM-SCORE INDEX AND ARTIFICIAL INTELLIGENCE MODELS
DOI:
https://doi.org/10.1590/SciELOPreprints.13882Keywords:
Metabolic syndrome, artificial intelligence, early diagnosis, primary health careAbstract
Metabolic syndrome is a public health problem, due to its late diagnosis and the great clinical variability that characterizes it. Their attention is limited by the lack of early detection tools. The objective of this research was to analyze the effectiveness of the SM-score computational index in the early detection of metabolic syndrome, which was designed using algorithmic processes. The study was conduced from January 2023 to June 2025, with a sample of 2000 patients who were provided with consultations, in primary care in Cuba. And clinical and biochemical information was obtained from medical records, which were processed with SPSS v27.1. The SM-score correlates strongly with conventional diagnostic methods, while diagnostic accuracy has been improved. Its development through a visual interface in Python and allowed the recognition of clinical patterns that help prevention. The combination of SM-score with computational systems is a useful tool for diagnosing metabolic syndrome at the primary care level.
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Copyright (c) 2025 José Antonio Alonso Viamonte, María Caridad Rodríguez Pérez

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