# 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
That is to say, in this test, there is insufficient evidence to demonstrate that the performance of the fusion model is significantly superior or inferior to that of other models, excluding the decision tree model. Notably, the comparison \(p\)-values of the fusion model are quite close to those of the LightGBM model, GBDT model, and XGBoost model. This similarity in \(p\)-values may imply that these models exhibit comparable stability across different data sets. Moreover, their performance in terms of data fitting and prediction accuracy is roughly on par, suggesting that these models can maintain a relatively consistent performance level under varying data conditions. The \(p\) value between the fusion model and the decision tree model is less than 0.05, which indicates that the performances of the fusion model and the decision tree model are significantly different. It can be speculated that the fusion model may be better than the decision tree model in performance, but the specific situation needs further analysis of the background and data characteristics of the problem. If the performance is improved significantly, the fusion model may be a better choice than the decision tree model. If \begin{table}
\begin{tabular}{c c c} \hline \hline
**Contrast Mode** & \(\mathbf{t}\) & \(\mathbf{p}\) \\ \hline Fusion model and LightGBM model & 1.637964 & 0.102573 \\ Fusion model and SVM model & 0.266811 & 0.789815 \\ Fusion model and KNN model & \(-\)0.619480 & 0.536112 \\ Fusion model and Decision Tree model & 1.990495 & 0.047526 \\ Fusion model and GBDT model & 1.511057 & 0.131922 \\ Fusion model and XGBoost model & 1.416791 & 0.157675 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(t\)-statistics and \(p\)-values of the fusion model compared with other models. it is not sensitive to performance differences, other models can be selected according to specific situation. However, compared to other models, the fusion model has the highest recall rate and an excellent F1 score performance. It balances accuracy and recall well, ensuring accurate predictions and effectively identifying positive cases. Thus, it has clear advantages in tasks needing high-level comprehensive model performance. Although the precision of the fusion model is slightly lower than that of the GBDT model, it is still at a high level. This indicates that the fusion model achieves high overall prediction accuracy and can accurately judge the majority of samples. The comprehensive performance advantage of the fusion model stems from its utilization of bootstrap sampling and weighted fusion of multiple basic models. This enables it to comprehensively leverage the strengths of different models while circumventing the limitations of any single model. From various indicators, it performs well in many aspects, and there is no obvious shortcoming, which reflects the effectiveness of the fusion strategy in improving the performance of the model. To analyze whether the fusion model is overfitted, we compare the disparities in accuracy, precision, recall, and the F1 score between the training set and the test set. Based on these differences, we then determine whether the model is overfitted. A common indicator of overfitting is that the larger the disparity between the performance metrics of the training set and those of the test set, the higher the likelihood of overfitting. The detailed data can be found in the Table 6 below. The differences among the overfitting indexes of the LightGBM, decision tree, and XGBost models are generally large. The performance difference of decision tree model between the training set and the test set is more obvious, and the problem of overfitting is more serious. This might be attributable to the fact that the decision tree model tends to generate a complex tree structure during the training process. This structure overfits the noise and specific details within the training data, consequently leading to a substantial deterioration in its performance on the test set.