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Predict high-risk diabetes patients for early intervention, reducing complications and costs.
Approximately 32 million people in Latin America live with diabetes. The prevalence of diabetes in the region is expected to increase significantly, with projections indicating a rise to 64 million by 2025 [1]. There is a recognized need for greater emphasis on preventive medicine to mitigate the impact of diabetes. This includes lifestyle interventions and public health campaigns to reduce the incidence of diabetes and its complications.
Not only that, due to the complexity of this deseases medical treatment is expensive for patients as well for the system. The healthcare systems in Latin America face significant challenges in managing diabetes due to the increasing prevalence and associated economic burdens. If current healthcare models remain unchanged, the financial resources required to address diabetes will continue to grow [4].
Various machine learning models, including decision trees, support vector machines, and neural networks, have been developed to predict diabetes. These models can achieve high accuracy by learning from historical data and identifying key predictors of diabetes, such as age, BMI, blood pressure, and glucose levels
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“DiabetesPredict” is a an algorithm trained to interpret aggregate health data, focusing on the relevant clinical factors. Their assessments guide public health efforts to manage the prevalence of diabetes and mitigate its financial burden on health systems.
The synthetic data set used for the AI model incorporates variables similar to those reported by the CDC in its “National Diabetes Statistics Report, 2020” (1) and considerations for the economic impact of diabetes explored by the American Diabetes Association (2) . The structure and values of the data set are intended to resonate with information on diabetes management and prevention, as discussed by Shrivastava et al. (3) and the role of self-care in diabetes control. The design is also based on studies examining the cost-effectiveness of diabetes interventions (4)(5).
Approximately 32 million people in Latin America live with diabetes. The prevalence of diabetes in the region is expected to increase significantly, with projections indicating a rise to 64 million by 2025 [1]. There is a recognized need for greater emphasis on preventive medicine to mitigate the impact of diabetes. This includes lifestyle interventions and public health campaigns to reduce the incidence of diabetes and its complications.
Not only that, due to the complexity of this deseases medical treatment is expensive for patients as well for the system. The healthcare systems in Latin America face significant challenges in managing diabetes due to the increasing prevalence and associated economic burdens. If current healthcare models remain unchanged, the financial resources required to address diabetes will continue to grow [4].
Various machine learning models, including decision trees, support vector machines, and neural networks, have been developed to predict diabetes. These models can achieve high accuracy by learning from historical data and identifying key predictors of diabetes, such as age, BMI, blood pressure, and glucose levels
‍
“DiabetesPredict” is a an algorithm trained to interpret aggregate health data, focusing on the relevant clinical factors. Their assessments guide public health efforts to manage the prevalence of diabetes and mitigate its financial burden on health systems.
The integration of machine learning and AI has revolutionized diabetes care by providing innovative approaches to prediction, monitoring, and personalized management. This integration allows for more accurate and timely interventions, improving patient outcomes [7].
The synthetic data set used for the AI model incorporates variables similar to those reported by the CDC in its “National Diabetes Statistics Report, 2020” (1) and considerations for the economic impact of diabetes explored by the American Diabetes Association (2) . The structure and values of the data set are intended to resonate with information on diabetes management and prevention, as discussed by Shrivastava et al. (3) and the role of self-care in diabetes control. The design is also based on studies examining the cost-effectiveness of diabetes interventions (4)(5).
