Publication: Técnicas matemáticas para diagnosis médica
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2022-02-14
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Abstract
Se quiere conseguir un modelo matemático para la realización de formularios fiables para diagnosticar una enfermedad. Expondremos cómo la regresión estadística es una fuerte herramienta para el análisis y relación de diversas variables. Más allá, se aspira a presentar fórmulas y algoritmos matemáticos esenciales en el proceso de la diagnosis médica.
Los estudios de casos y controles sirven para obtener los odd ratios y sus riesgos absolutos y relativos correspondientes. Nos basaremos en las probabilidades que nos proporcionan los datos de estos estudios para llegar a un modelo logístico en términos de probabilidades logarítmicas.
Inicialmente, consideramos datos relacionados con el cáncer de mama. A partir de los métodos mencionados anteriormente podemos considerar igualmente datos COVID-19. Llegamos a formularios análogos consiguiendo relacionar ciertas circunstancias y causas con peores pronósticos. Finalmente, se pueden comprobar y testar datos de pacientes provenientes de bases de datos.
We want to achieve mathematical models to create reliable forms for medical diagnosis. We pretend to exhibit why regression analysis is a strong analytic tool to estimate and relate a set of variables. Beyond that, we aim to present essential mathematical formulas and algorithms for the medical diagnosis process. Case and control studies are useful to obtain odd ratios and the corresponding absolute and relative risks. We base our report on the probabilities obtained from the data belonging to those studies to lead to a logistic model in terms of logarithms probabilities. We start our study considering breast cancer data. Based on the previously mentioned methods, we can also tackle covid data. We obtain similar forms, reaching relations between some circumstances and causes with worst outcomes. From this, we can verify and test data patients from databases.
We want to achieve mathematical models to create reliable forms for medical diagnosis. We pretend to exhibit why regression analysis is a strong analytic tool to estimate and relate a set of variables. Beyond that, we aim to present essential mathematical formulas and algorithms for the medical diagnosis process. Case and control studies are useful to obtain odd ratios and the corresponding absolute and relative risks. We base our report on the probabilities obtained from the data belonging to those studies to lead to a logistic model in terms of logarithms probabilities. We start our study considering breast cancer data. Based on the previously mentioned methods, we can also tackle covid data. We obtain similar forms, reaching relations between some circumstances and causes with worst outcomes. From this, we can verify and test data patients from databases.
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