João Carlos Pereira Alves 
, Ricardo Menezes Salgado , Iago Augusto Carvalho & Eric Batista Ferreira
Abstract: Congestive heart failure (CHF) is a serious medical condition associated with high mortality rates. To improve prognosis and treatment, exploring new strategies is essential. This study investigates the use of electronic medical records to train machine learning models in predicting survival and hospitalization time for patients with CHF. Using data from 299 patients collected in Faisalabad, Pakistan, a suite of algorithms was evaluated, including MLP, logistic regression, Random Forests, decision tree, k-Nearest Neighbors, Naive Bayes, and Gradient Boosting. The methodology employed the SMOTE technique for class balancing and a rigorous 10-fold stratified cross-validation for performance evaluation. The Random Forest model emerged as the top performer, achieving a mean accuracy of 0.80 (±0.05) and an F1-Score of 0.80 (±0.05) in predicting patient survival. Statistical significance tests confirmed that the superiority of the Random Forest over the worst-performing model (MLP) is statistically significant (p < 0.05). For the prediction of hospitalization time, an error rate of 26% was observed. These findings underscore the statistically validated potential of machine learning models in predicting clinical outcomes in patients with CHF, representing an innovative approach to improve diagnostic efficiency and reduce the impact of CHF on public health.
Keywords: Congestive Heart Failure, Machine Learning, Clinical Prediction, Hospitalization Time, Survivability.
DOI code: 10.21528/lnlm-vol23-no2-art2
PDF file: vol23-no2-art2.pdf
BibTex file: vol23-no2-art2.bib