Software Development Model for Predicting Survival of Heart Failure Patient Using Multi-Algorithm Method

Heart failure (HF) is a major global health issue and a leading cause of hospitalization as well as mortality, specifically among the elderly. Accurate prediction and continuous monitoring of HF patient survival are essential for effective clinical decision-making. Therefore, this study aimed to develop a web-based software system that incorporates multi-algorithm, Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Neural Network (NN), to predict patient survival using 12 clinical variables. The software enabled efficient diagnosis and monitoring by generating predictive outcomes based on patient data. In the evaluation phase, the NB algorithm achieved the highest accuracy of 88.21%, followed by RF (85.11%), NN (82.36%), SVM (76.11%), and KNN (62%). The major originality of this study resided in combining multi-algorithm into a unified diagnostic platform, offering practical and low-cost decision support for healthcare providers. In contrast to previous studies that focused on developing standalone model, this analysis produced a functional application directly implemented in clinical environments. Relating to the analysis, future studies should aim to expand the dataset, incorporate real-time hospital data, and use explainable AI to improve interpretability, scalability, as well as trustworthiness in medical decision support systems.