CRP and congestive heart failure: The predictive accuracy of currently available machine learning tools ranges from 81 to 96%, based on parameters such as age, oxygen saturation, lactate dehydrogenase, urinary carbamide nitrogen, CRP, impaired kidney function, chronic kidney disease, medical history of coronary heart disease and chronic heart failure.