electronic health records-based dataset, with an accuracy of 91% for the classification of CHF [5]. Utilizing the dataset of a public database, MIMIC-III, [PERSON] et al. used an XGBoost algorithm as their classifier when building a predictive model for in-hospital mortality that had good calibration, with an AUC score of 0.8416 compared to an AUC of 0.7747 for their Get With the Guidelines-Heart Failure (GWTG-HF) risk score model [6]. Designing optimal machine learning-based models is challenging due to the variety of datasets and the complexity of the algorithms used for building predictive models [7]. To obtain the optimal model, the hyper-parameter of the model needs to be tuned to fit a specific dataset. Because a clinical-based dataset consists of complex variables, tuning the hyper-parameter manually is extremely expensive and time-consuming [8]. Applying an optimization method to find the ideal hyper-parameter for each algorithm provides an efficient way to tune the model. Grid Search has become popular for optimizing machine learning and has been used in multiple studies, specifically for the prediction of heart disease and heart failure [9; 10; 11]. The simplicity of the Grid Search method allows researchers to optimize their models [11]. [PERSON] et al. utilized Grid Search to obtain optimal configurations for their heart disease prediction models, resulting in slight improvement in terms of accuracy [12]. However, due to the brute force characteristic of Grid Search, the optimization process takes more time in some cases. [PERSON] and [PERSON] conducted a comparative study of the Grid Search and Random Search optimization methods, reporting that Random Search needed less processing time and provided better performance than Grid Search [13]. Moreover, [PERSON] et al. proved that Random Search optimization was able to adequately tune their hybrid deep neural networks model for predicting coronary heart disease [14]. [PERSON] and [PERSON] et al. promoted the use of Bayesian hyper-parameter optimization to optimize ensemble learning-based models in the field of medical informatics, including cardiovascular disease prediction [15; 16]. They reported that the Bayesian optimization method provided better stability when finding the optimal hyper-parameter than the Grid and Random Search methods. However, their study did not carry out a performance comparison of the three optimization methods. In this study, we used a comparative analysis approach with two methods commonly used for Bayesian-based optimization to build predictive models for heart failure risk prediction based on a real-patient dataset. While the Grid Search and Randomized Search methods are more common and easier to apply, we found that Bayesian Search has the potential to provide better performance. This study is a classification metrics-based evaluation performed to compare the above three optimization methods. The study was carried out as follows:  
1. The Support Vector Machine, Random Forest, and eXtreme Gradient Boosting algorithms were used to evaluate and compare the effectiveness of the Grid, Random, and Bayesian search methods in determining the ideal configuration for optimizing the performance of a model. 2. The robustness of the proposed models was examined through comprehensive 10-fold cross-validation. While several recent studies have applied machine learning techniques to predict heart failure outcomes, most have focused either on single-model performance or on limited aspects of hyper-parameter tuning [3; 4; 5; 6; 9; 10; 12]. In contrast, our study provides a unique, comprehensive comparison of three hyper-parameter optimization methods--Grid Search, Randomized Search, and Bayesian Search--across three distinct algorithms (SVM, RF, and XGBoost) while also systematically investigating the impact of multiple imputation techniques on model performance.