Examinando por Autor "Barquero-Perez, Oscar"

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Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest
(MDPI, 2021-06) Garcia-Carretero, Rafael; Holgado-Cuadrado, Roberto; Barquero-Perez, Oscar
Nonalcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome and is the most common cause of chronic liver disease in developed countries. Certain conditions, including mild inflammation biomarkers, dyslipidemia, and insulin resistance, can trigger a progression to nonalcoholic steatohepatitis (NASH), a condition characterized by inflammation and liver cell damage. We demonstrate the usefulness of machine learning with a case study to analyze the most important features in random forest (RF) models for predicting patients at risk of developing NASH. We collected data from patients who attended the Cardiovascular Risk Unit of Mostoles University Hospital (Madrid, Spain) from 2005 to 2021. We reviewed electronic health records to assess the presence of NASH, which was used as the outcome. We chose RF as the algorithm to develop six models using different pre-processing strategies. The performance metrics was evaluated to choose an optimized model. Finally, several interpretability techniques, such as feature importance, contribution of each feature to predictions, and partial dependence plots, were used to understand and explain the model to help obtain a better understanding of machine learning-based predictions. In total, 1525 patients met the inclusion criteria. The mean age was 57.3 years, and 507 patients had NASH (prevalence of 33.2%). Filter methods (the chi-square and Mann–Whitney–Wilcoxon tests) did not produce additional insight in terms of interactions, contributions, or relationships among variables and their outcomes. The random forest model correctly classified patients with NASH to an accuracy of 0.87 in the best model and to 0.79 in the worst one. Four features were the most relevant: insulin resistance, ferritin, serum levels of insulin, and triglycerides. The contribution of each feature was assessed via partial dependence plots. Random forest-based modeling demonstrated that machine learning can be used to improve interpretability, produce understanding of the modeled behavior, and demonstrate how far certain features can contribute to predictions.
Cystatin C as a predictor of cardiovascular outcomes in a hypertensive population
(Springer Science and Business Media LLC, 2017-12) Garcia-Carretero, Rafael; Vigil-Medina, Luis; Barquero-Perez, Oscar; Goya-Esteban, Rebeca; Mora-Jimenez, Inmaculada; Soguero-Ruiz, Cristina; Ramos-Lopez, Javier
Identification of clinically relevant features in hypertensive patients using penalized regression: a case study of cardiovascular events
(Springer Science and Business Media LLC, 2019-07-25) Garcia-Carretero, Rafael; Barquero-Perez, Oscar; Mora-Jimenez, Inmaculada; Soguero-Ruiz, Cristina; Goya-Esteban, Rebeca; Ramos-Lopez, Javier
Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations
(Springer Science and Business Media LLC, 2019-12) Garcia-Carretero, Rafael; Vigil-Medina, Luis; Barquero-Perez, Oscar; Ramos-Lopez, Javier
Use of a K-nearest neighbors model to predict the development of type 2 diabetes within 2 years in an obese, hypertensive population
(Springer Science and Business Media LLC, 2020-05) Garcia-Carretero, Rafael; Vigil-Medina, Luis; Mora-Jimenez, Inmaculada; Soguero-Ruiz, Cristina; Barquero-Perez, Oscar; Ramos-Lopez, Javier
Prediabetes is a type of hyperglycemia in which patients have blood glucose levels above normal but below the threshold for type 2 diabetes mellitus (T2DM). Prediabetic patients are considered to be at high risk for developing T2DM, but not all will eventually do so. Because it is difficult to identify which patients have an increased risk of developing T2DM, we developed a model of several clinical and laboratory features to predict the development of T2DM within a 2-year period. We used a supervised machine learning algorithm to identify at-risk patients from among 1647 obese, hypertensive patients. The study period began in 2005 and ended in 2018. We constrained data up to 2 years before the development of T2DM. Then, using a time series analysis with the features of every patient, we calculated one linear regression line and one slope per feature. Features were then included in a K-nearest neighbors classification model. Feature importance was assessed using the random forest algorithm. The K-nearest neighbors model accurately classified patients in 96% of cases, with a sensitivity of 99%, specificity of 78%, positive predictive value of 96%, and negative predictive value of 94%. The random forest algorithm selected the homeostatic model assessment–estimated insulin resistance, insulin levels, and body mass index as the most important factors, which in combination with KNN had an accuracy of 99% with a sensitivity of 99% and specificity of 97%. We built a prognostic model that accurately identified obese, hypertensive patients at risk for developing T2DM within a 2-year period. Clinicians may use machine learning approaches to better assess risk for T2DM and better manage hypertensive patients. Machine learning algorithms may help health care providers make more informed decisions.