# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 5 Experimental Results and Analysis
### Discussion on Model Deployment with Incomplete External Verification  
Before deploying the model related to heart diseases based on this data set in the real world, a series of rigorous clinical validation steps are needed. These steps should comprehensively evaluate key aspects, such as the algorithm foundation, data processing, and output interpretation of the model, to ensure its accuracy, reliability, and security. For example, during internal verification, data sets should be divided into a training set, a validation set, and a test set according to a specific proportion. Generally, the training set is utilized for model training. The validation set is employed for adjusting model parameters and selecting the optimal model. The test set is then used to evaluate the final performance of the model. Cross-validation, such as KFold cross-validation, is also of great importance. Through multiple divisions of the data set, followed by training and validation processes, the stability and generalization ability of the model can be effectively assessed, and the bias resulting from data division can be mitigated. Multi-aspect validation is of utmost importance. In particular, external validation necessitates the use of an independent data set for validation. That is, independent heart disease data sets sourced from other medical institutions or different regions are employed to validate the model. This approach ensures that the model can sustain good performance across diverse data distributions. These external data sets should have similar characteristics to the target application scenario, but they are independent from the training data set to verify the generality of the model. Multi-center verification is a crucial aspect that cannot be overlooked. It requires validation across numerous distinct clinical centers, which vary in geographical location, patient demographics, and medical practices, among other factors. Multi-center verification enables a more comprehensive assessment of the model's performance across diverse clinical environments. It also helps to identify potential variations in the model's performance attributable to factors such as regional differences and disparities in medical standards. In addition, both performance evaluation and clinical practicality assessment are of great significance. It is essential to compare this model with existing diagnostic methods to ascertain whether it offers added clinical value. For example, there is a need to assess whether the early detection of diseases can be realized, the accuracy of diagnosis can be improved, or the cost of diagnosis can be reduced. At the same time, there is a need to investigate the influence of the prediction results of the model on clinical decision making, such as whether it can help doctors make more accurate treatment plans and judge the prognosis of patients. In practical application, the potential impact of the model on medical quality and patient outcome can also be evaluated by simulating clinical scenarios. Finally, the importance of ethical and legal review should not be underestimated. It is imperative to adhere strictly to relevant ethical guidelines and legal regulations to ensure that the deployment and utilization of the model comply with the provisions of medical laws and regulations. Notably, given that the external verification of the model remains incomplete, extreme caution must be exercised when contemplating its real-world deployment, even after a series of processes such as internal verification and multi-center verification have been successfully carried out. In the future, only upon successfully passing a comprehensive and rigorous set of clinical verification procedures can it be ensured that the model is ready for deployment in real-world scenarios. If the model is precipitated deployed without comprehensive verification, in the event that issues such as misdiagnosis and data leakage occur, it will not only severely undermine the interests of patients but also give rise to legal disputes.