Introduction
HF, ’ disease, decompensation, Heart failure, COPD
COPD EXACERBATION, CHRONIC DISEASES, CHRONIC OBSTRUCTIVE PULMONARY DISEASE, COPD, HEART FAILURE, DISEASES
Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are two chronic diseases with the greatest adverse impact on the general populationThe usual approach of the methods developed to date to detect early decompensation of both diseases is based on ambulatory monitoring of clinical parameters using predictive models or diagnostic algorithms applied continuously or intermittentlyAlthough various algorithms have been developed to date, very few overcome the sensitivity (S) and specificity (E) threshold of 80%Our group previously developed and reported diagnostic algorithms for the detection of COPD exacerbation (S: 90%, E: 89%) and decompensated HF (S: 85%, E: 75%) based on noninvasive monitoring of physiological parameters of patients (HR, Ox and walking distance) in compensated and decompensated phases of their diseasesTo overcome these limitations, we believe that machine learning (ML) techniques can be useful. This approach allows more efficient and individualized use of the vast amount of data produced from continuous monitoring of physiological parametersIn this study, we report the diagnostic performance of diagnostic algorithms based on physiological parameters (HR and Ox) and developed with ML techniques to classify patients’ disease phases (compensated or decompensated). The recommended guidelines for reporting this type of study have been considered