# 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
In general, the LightGBM model with bootstrap sampling and weighted fusion demonstrates its advantages in multiple aspects by virtue of its distinctive processing and fusion techniques. Compared with other common models, it has better performance in different degrees, especially in the recall rate. The comparative analysis of the ROC curve is a highly reliable approach for assessing the performance of classifiers. It enables us to discern the performance disparities among multiple classifiers and make an informed decision in selecting the most appropriate classifier to address practical issues. The subsequent figure illustrates the ROC curve obtained from the test data set. The Figure 11 depicts the ROC curves of several models, along with their corresponding AUC values. These visual and numerical indicators are employed to assess the capacity of various models to discriminate between positive cases (for example, individuals with heart disease) and negative cases (such as those without heart disease). The horizontal axis represents the FPR, which is defined as the ratio of misclassifying negative cases as positive cases. On the other hand, the vertical axis denotes the TPR, which refers to the proportion of accurately classifying positive cases. The dotted black line in the figure represents random guess. In the context of evaluating model performance, the closer the curve of the model is to the upper left corner of the chart, the better its performance. From the shape of ROC curve, the curve of the paper model, the fusion model, is close to the upper left corner, which is an ideal model representation area. It can maintain a high true positive rate in the whole range of false positive rate. This indicates that in practical  
Figure 11: The ROC curve of the test set. applications, the hybrid model employed in this paper has the ability to effectively identify patients with actual heart disease. In other words, it can maintain a high true positive rate (TPR), thus maximizing the accurate detection of positive cases. At the same time, it aims to significantly minimize the occurrence of false positives and, thereby, avoid wrongly classifying healthy individuals as heart disease patients. The AUC of the mixed model used in this paper is 0.92, which is a very high level. AUC value represents the comprehensive ability of the model to distinguish between positive cases with heart disease and negative cases without heart disease under different classification thresholds. The curves of KNN (AUC = 0.74) and the decision tree (AUC = 0.78) are near the random guess line, indicating weak discriminative power. The low AUC values suggest that these models have certain limitations and are likely to make classification errors when differentiating between positive and negative cases. In contrast, SVM (AUC = 0.91), GBDT (AUC = 0.91), XGBoost (AUC = 0.90), and LightGBM (AUC = 0.91) perform excellently. Compared with these well-performing models, the fusion model has more prominent advantages, with a relatively high AUC. This implies that the fusion model is highly likely to effectively curtail the incidence of misclassification and decrease the false positive rate. As a consequence, it stands a greater chance of ensuring the rational and efficient allocation of medical resources. The paired t-test is used to test the significance of the fusion model and other commonly used models. In the paired \(t\)-test, the \(t\)-statistics and \(p\)-value data of the fusion model compared with other models are shown in the following Table 5.  
From the significance test, the \(p\)-values between the fusion model and other models, except the decision tree model, are greater than 0.05. This indicates that, from the significance test index, there is no significant difference in performance between the fusion model and other models except the decision tree model.