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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). Approximately 80 million adults in the U.S. meet the criteria for MetS, leading to an average of 60% higher annual healthcare utilization and costs compared to those without MetS (3). Individuals with MetS are five times more likely to develop diabetes, and the condition contributes to a total annual healthcare cost exceeding $220 billion (4)(5). 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).
To assist in this effort, 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). Approximately 80 million adults in the U.S. meet the criteria for MetS, leading to an average of 60% higher annual healthcare utilization and costs compared to those without MetS (3). Individuals with MetS are five times more likely to develop diabetes, and the condition contributes to a total annual healthcare cost exceeding $220 billion (4)(5). 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).
To assist in this effort, 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.
