# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 5 Experimental Results and Analysis
### Comparative Analysis of Model Effects
#### 5.2.3 Experiment and Comparison of Fusion Model
When compared with the LightGBM, decision trees, and XGBoost models, the disparity in the overfitting index of the fusion model is significantly smaller than that of the basic models. This indicates that the overfitting issue has been substantially mitigated. However, there still exist certain discrepancies between the performance of the training set and that of the test set. Moreover, differences can also be observed in terms of accuracy, precision, recall, and the F1 score, suggesting that the fusion model still exhibits some degree of overfitting. The reason may be that the size of the data in this data set is not large, and the original data have not been processed using data enhancement technology, which could increase the diversity of the data and expand the scale of the data set. In addition, during the model training process, the number of training epochs and the learning rate might not have been set appropriately. This could potentially cause the model to be over-optimized for the training set. As a result, the model gradually adapts to the noise and minor fluctuations present in the training data, ultimately leading \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **Accuracy** \\ **Difference** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **Difference** \\ \end{tabular} & \
\begin{tabular}{c} **Recall** \\ **Difference** \\ \end{tabular} &
\begin{tabular}{c} **F1 Difference** \\ \end{tabular} \\ \hline Fusion model & 0.126202 & 0.135431 & 0.100929 & 0.119140 \\ LightGBM & 0.170290 & 0.175676 & 0.146853 & 0.161512 \\ SVM & \(-\)0.008093 & 0.020677 & 0.003372 & 0.012340 \\ KNN & 0.075918 & 0.096855 & 0.067899 & 0.083494 \\ Decision Tree & 0.242753 & 0.224638 & 0.251748 & 0.238434 \\ GBDT & 0.098199 & 0.091612 & 0.103956 & 0.097608 \\ XGBoost & 0.170290 & 0.175676 & 0.146853 & 0.161512 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of overfitting indexes of various models. to overfitting. Especially on small sample data sets, the model is more likely to converge to a local optimum quickly. This is also the deficiency of the paper model, which needs further study. There is little difference among the overfitting indexes of the SVM model, the KNN model, and the GBDT model, which indicates that the overfitting phenomenon is not obvious. This could be attributed to the fact that the SVM model effectively circumvents the overfitting issue by employing the kernel function during data processing. This enables the model to exhibit a more consistent performance across diverse data sets. However, the SVM model does not perform well in accuracy, precision, recall rate, or the F1 score. KNN model is related to the selection of the k value and data distribution. If the value of k is not chosen properly, the model may be too sensitive to local data, leading to overfitting. If the GBDT model undergoes an excessive number of iterations, or if the learning rate is set inaccurately, overfitting may occur. Generally speaking, the degree of overfitting is different for different models. Although the fitting problem of the fusion model is smaller than that of the selected basic model, LightGBM still has an overfitting issue. In practical applications, it is essential to choose an appropriate model in accordance with specific requirements and the characteristics of the data at hand. Additionally, corresponding measures should be taken to mitigate the overfitting issue. By doing so, the generalization ability and prediction accuracy of the model can be enhanced. Of course, there are some limitations in the fusion model. For example, it increases the computational complexity. The fusion model requires the training, sampling, and weighting of multiple basic models. This undoubtedly elevates the computational complexity of the model and prolongs its training time. This computational burden may become a limiting factor for its application in large-scale data processing or scenes that require high real-time performance.