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AI can help healthcare organizations (HCOs) identify individuals at-risk for developing Metabolic syndrome (MetS).
Metabolic syndrome (MetS) is a cluster of risk factors—central obesity, insulin resistance, dyslipidemia, and hypertension—that significantly increases the risk of cardiovascular disease and type 2 diabetes. It also often includes conditions such as excessive blood clotting and chronic low-grade inflammation, and has been linked to various cancers including breast, pancreatic, colon, and liver cancer (1)(2).
The prevalence of metabolic syndrome in Latin America ranges from 20% to 40% among adults, with higher rates observed in urban areas compared to rural regions. Factors such as lifestyle changes, urbanization, and dietary habits contribute to these high prevalence rates [3]. Despite its prevalence and serious implications, public awareness of MetS is low, with less than 15% of those at risk or with diabetes aware of the condition. Increasing awareness and early identification are crucial, as an additional 104 million people are at risk of developing MetS (6).
AI-powered tools can enhance the accuracy and efficiency of screening processes by automating the analysis of clinical parameters such as blood pressure, glucose levels, and lipid profiles. This can help in early detection and timely intervention.
An AI model has been developed to predict the occurrence of MetS in individuals. It leverages physiological and lifestyle variables, offering healthcare providers a means to identify and support patients in high-risk categories for MetS with appropriate preventive measures.
The synthetic dataset was constructed by referencing extensive research and data from peer-reviewed studies and healthcare databases to closely replicate authentic clinical cases. Sources such as Steinberg et al. (1), NHLBI (2), O'Neill and O'Driscoll (3), Boudreau et al. (4), Yu et al. (5), and Lewis et al. (6), provided the necessary frameworks for model attributes, ensuring accurate MetS prediction.
Metabolic syndrome (MetS) is a cluster of risk factors—central obesity, insulin resistance, dyslipidemia, and hypertension—that significantly increases the risk of cardiovascular disease and type 2 diabetes. It also often includes conditions such as excessive blood clotting and chronic low-grade inflammation, and has been linked to various cancers including breast, pancreatic, colon, and liver cancer (1)(2).
The prevalence of metabolic syndrome in Latin America ranges from 20% to 40% among adults, with higher rates observed in urban areas compared to rural regions. Factors such as lifestyle changes, urbanization, and dietary habits contribute to these high prevalence rates [3]. Despite its prevalence and serious implications, public awareness of MetS is low, with less than 15% of those at risk or with diabetes aware of the condition. Increasing awareness and early identification are crucial, as an additional 104 million people are at risk of developing MetS (6).
AI-powered tools can enhance the accuracy and efficiency of screening processes by automating the analysis of clinical parameters such as blood pressure, glucose levels, and lipid profiles. This can help in early detection and timely intervention.
An AI model has been developed to predict the occurrence of MetS in individuals. It leverages physiological and lifestyle variables, offering healthcare providers a means to identify and support patients in high-risk categories for MetS with appropriate preventive measures.
AI optimizes the allocation of healthcare resources by identifying high-risk individuals and prioritizing them for preventive measures.
AI facilitates early detection of metabolic syndrome through predictive analytics, allowing for timely interventions that prevent the progression to more severe and costly health conditions such as cardiovascular diseases and diabetes.
AI is being integrated into the prevention, diagnosis, and treatment of metabolic syndrome, offering a transformative approach to managing this public health threat. AI can help overcome the limitations of traditional management methods by providing more precise and personalized healthcare solutions [7].
The synthetic dataset was constructed by referencing extensive research and data from peer-reviewed studies and healthcare databases to closely replicate authentic clinical cases. Sources such as Steinberg et al. (1), NHLBI (2), O'Neill and O'Driscoll (3), Boudreau et al. (4), Yu et al. (5), and Lewis et al. (6), provided the necessary frameworks for model attributes, ensuring accurate MetS prediction.
