Purpose: Using a data and machine learning approach, from classical to complex, we aim to approximate the relationship between factors such as behavioral, social or comorbidity and the ejection fraction for hospitalized patients. To measure how much the independent variables influence the left ventricular ejection fraction (LVEF), classification models will be made and the influences of the independent variables will be interpreted. Through the data obtained, it is desired to improve the management of patients with heart failure (treatment, monitoring in primary medicine) in order to reduce morbidity and mortality.Patients and Methods: In this study, we enrolled 201 patients hospitalized withdecompensated chronic heart failure. The models used are extreme gradient boosting (XGB) and logistic regression (LR). To have a deeper analysis of the independent variables, their influences will be analyzed in two ways. The first is a modern technique, Shapley values, from game theory, adapted in the context of Machine Learning for XGB;and the second, the classical approach, is by analysis of Logistic Regression coefficients.Results: The importance of several factors related to behavior, social and diabetes are measured. Smoking, low education and obesity are the most harmful factors, while diabetes controlled by diet or medication does not significantly affect LVEF, indeed, there is a tendency to increase the LVEF. Conclusions: Using machine learning techniques, we can better understand to what extent certain factors affect LVEF in this sample. Following furtherstudies on larger groups and from different regions, prevention could be better understood and applied.
Hypertension frequently coexists with obesity, diabetes, hyperlipidemia, or metabolic syndrome, anditsassociation with cardiovascular disease is well established. The identification and management of these risk factors is an important part of overall patient management. In this paper, we find the most relevant patterns of hospitalized patients with cardiovascular diseases, consideringaspects of their comorbidities, such as triglycerides, cholesterol, diabetes, hypertension, and obesity. To find the most relevant patterns, several clusterizations were made, playing with the dimensions of comorbidity and the number of clusters. There are three main patient types who require hospitalization: 20% whose comorbidities are not so severe, 44% with quite severe comorbidities, and 36% with fairly good triglycerides, cholesterol, and diabetes but quite severe hypertension and obesity. The comorbidities, such as triglycerides, cholesterol, diabetes, hypertension, and obesity, were observed in different combinations in patients upon hospital admission.
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