Automated learning applied to early detection of type 2 Diabetes Mellitus: The Case of Saltillo, México.
Keywords:
Type 2 Diabetes Mellitus, Automated Learning, Predictive modelsAbstract
Over the last few decades, health systems around the world have registered a significant growth in the number of people diagnosed with Diabetes Mellitus, which can be described as a condition associated with unhealthy living conditions such as obesity and/or overweight, a sedentary life and a diet rich in sugars and fats, as well as a predisposition due to genetic factors. Given the lack of early detection strategies, those affected are diagnosed once they have developed the disease and, therefore, present obvious symptoms. This article presents the results of a study, where clinical information was collected on people over 15 years of age with risk factors for Type 2 Diabetes Mellitus, treated at the Mexican Institute of Social Security (IMSS), in the City of Saltillo, Coahuila, México. This database of 1820 records made it possible to explore different Automated Learning tools that allow early detection of diseases, such as Supervised Learning models like Naive Bayes and Random Forest, which, when trained, achieved more than 73% sensitivity in the prediction of this disease.
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