Propuesta de modelo de análisis de datos para categorizar el nivel de riesgo cardiovascular en el departamento del Atlántico
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Fecha
2021
Autores
Consuegra González, Luis Rodrigo
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Ediciones Universidad Simón Bolívar
Facultad de Ingenierías
Facultad de Ingenierías
Resumen
La presente propuesta de trabajo de investigación de maestría tiene como objetivo
proponer un modelo basado en análisis de datos para categorizar el nivel de
riesgo cardiovascular que apoye en la generación de planes para la prevención y
control de estas enfermedades en el departamento del Atlántico. La investigación
presentó un enfoque mixto, de carácter aplicado, con diseño experimental y una
población de 10.270 personas, de las cuales se tomaron 6218 personas en la
muestra. En primera medida, se estableció el modelo PROCAM, de acuerdo con
investigaciones previas, como el recomendado para el contexto del departamento
del Atlántico, en segunda medida, se hallaron como variables intervinientes en el
modelo el sexo, edad, tabaquismo, antecedentes familiares (muerte por infarto
agudo de miocardio u otra enfermedad cardiovascular), imc (índice masa
corporal), diabetes mellitus, presión arterial sistólica y diastólica, colesterol HDL,
colesterol LDL y triglicéridos. Como resultado de la investigación se espera que
mediante la implementación del modelo se tenga una constante actualización de
los datos de las historias clínicas para realizar análisis y filtros de la población
estimada con riesgo en la realización de estrategias focalizadas de prevención de
los pacientes detectados.
The present proposal of master's research work aims to propose a model based on data analysis to categorize the level of cardiovascular risk that supports the generation of plans for the prevention and control of these diseases in the department of Atlántico. The research presented a mixed approach, of an applied nature, with an experimental design and a population of 10,270 people, of which 6,218 people were taken in the sample. In the first measure, the PROCAM model was established, in accordance with previous research, such as the one recommended for the context of the department of Atlántico, in the second measure, sex, age, smoking, family history (death due to acute myocardial infarction or other cardiovascular disease), BMI (body mass index), diabetes mellitus, systolic and diastolic blood pressure, HDL cholesterol, LDL cholesterol and triglycerides. As a result of the research, it is expected that through the implementation of the model there will be a constant update of the data of the medical records to perform analyzes and filters of the estimated population at risk in the implementation of focused prevention strategies of the detected patients.
The present proposal of master's research work aims to propose a model based on data analysis to categorize the level of cardiovascular risk that supports the generation of plans for the prevention and control of these diseases in the department of Atlántico. The research presented a mixed approach, of an applied nature, with an experimental design and a population of 10,270 people, of which 6,218 people were taken in the sample. In the first measure, the PROCAM model was established, in accordance with previous research, such as the one recommended for the context of the department of Atlántico, in the second measure, sex, age, smoking, family history (death due to acute myocardial infarction or other cardiovascular disease), BMI (body mass index), diabetes mellitus, systolic and diastolic blood pressure, HDL cholesterol, LDL cholesterol and triglycerides. As a result of the research, it is expected that through the implementation of the model there will be a constant update of the data of the medical records to perform analyzes and filters of the estimated population at risk in the implementation of focused prevention strategies of the detected patients.
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Palabras clave
Enfermedad cardiovascular, Factores de riesgos cardiovasculares, Riesgo cardiovascular, Modelos de predicción cardiovascular, Cardiovascular disease, cardiovascular risk factors, cardiovascular risk, cardiovascular prediction models