Risk stratification in heart failure using artificial neural networks.
Accurate risk stratification of heart failure patients is critical to improve management and outcomes. Heart failure is a complex multisystem disease in which several predictors are categorical. Neural network models have successfully been applied to several medical classification problems. Using a simple neural network, we assessed one-year prognosis in 132 patients, consecutively admitted with heart failure, by classifying them in 3 groups: death, readmission and one-year event-free survival. Given the small number of cases, the neural network model was trained using a resampling method. We identified relevant predictors using the Automatic Relevance Determination (ARD) method, and estimated their mean effect on the 3 different outcomes. Only 9 individuals were misclassified. Neural networks have the potential to be a useful tool for making prognosis in the domain of heart failure.
Atienza
F
F
Cardiology Department, University General Hospital, Valencia, Spain. fatienzaf@meditex.es
Martinez-Alzamora
N
N
De Velasco
J A
JA
Dreiseitl
S
S
Ohno-Machado
L
L
D018572:Disease-Free Survival; D006333:Heart Failure / Q000145:classification* / Q000401:mortality; D006801:Humans; D016571:Neural Networks, Computer; D010359:Patient Readmission; D011379:Prognosis; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity