Modelo de predicción para enfermedades de insuficiencia cardiaca mediante razonamiento basado en casos Machine Learning

Abstract

Machine Learning is an important field for Artificial Intelligence (AI) in which, through an algorithm, it allows devices to learn without the need to be explicitly programmed to solve a specific task. Based Reasoning in Cases (RBC) is that process that tries to simulate human behavior regarding decision making based on a case and thereby learn from the experience of that case. It has proven to be an important tool for the medical field since it helps diagnose patients through a classification of presence or attention of disease. The present investigation is of a quasi-experimental Type, since its weeks totals control of the study variables, using experimental and control groups. In which the approach is considered a mixed investigation, since quantitative and qualitative data will be systematically integrated, because the medical records necessary to record the patient’s history contain numerical and descriptive data, algorithm and test, real data will be taken from the Cardiology patients of the REDIMA medical center.