GradientBoosting Diagnosing Microcirculatory Ischemic Heart Disease Using Exhaled Breath Predictors

BackgroundIschemic heart disease and the related sequalae remains the leading cause of mortality and morbidity globally. The poor diagnostic accuracy and availability of early screening methods are the leading objectives. AimsTo assess the diagnostic capabilities of the machine leaning model in the diagnosis of ischemic heart disease using the exhaled breath analysis predictors. Materials and methodsA single center prospective study involved participants with vs without stress induced myocardial perfusion defect. All the participants underwent real-time breath analysis using a PTR-TOF-MS-1000, bicycle ergometry test, and multidetector computed tomography (MDCT) of the coronary arteries with myocardial perfusion assessment. The obtained exhaled breath analysis data were analyzed using machine learning model. For statistical analysis used programme Statistica, SPSS, and python. ResultsAn 80 participants divided into 31 with positive stress-induced myocardial perfusion defect vs 49 without. The diagnostic features of the built model in compare to the reference the MDCT, AUC 86 % (95% confidence interval, [0.7805-0.9338]), sensitivity 0.6129 (95 % CI, [0.4414-0.7844] ), and specificity 0.8367 (95 % CI [0.7332-0.9402]). ConclusionThe Gradient Boosting model shows fascinating results using the exhaled breath analysis in the diagnosis of ischemic heart disease. However, further investigations on a larger sample size are required to uncover the hidden part of the plot.