## 4 Results and Discussion  
As discussed earlier, this paper proposes a novel approach, named the squirrel search-optimized gradient-boosted decision tree (SS-GBDT), which is based on machine learning (ML) and big data analytics (BDA). As such, it is important to validate the performance of the proposed SS-GBDT against the state of the art. For comparison with existing literature, several performance measures such as accuracy, precision, etc., are reported in this section. The ratio of all forecasts to corrected predictions Is what Equation (10), which calculates accuracy, is defined as:  
\[Accuracy=\frac{(\mathit{true\;negative}+\mathit{true\;positive})}{(\mathit{ true\;positive}+\mathit{false\;positive}+\mathit{true\;negative}+\mathit{false\;negative})}. \tag{10}\]  
The precision may be determined by solving Equation (11), which is shown below. \[Precision=\frac{\mathit{true\;positive}}{(\mathit{true\;positive}+\mathit{ false\;positive})}. \tag{11}\]  
Calculating the recall is possible due to Equation (12), which can be found here:  
\[Recall=\frac{\mathit{true\;positive}}{(\mathit{true\;positive}+\mathit{ false\;negative})}. \tag{12}\]Similarly, the F-measure can be calculated based on Equation (13):  
\[F-measure=\frac{2\times Precision\times Recall}{Precision+Recall}. \tag{13}\]  
Based on the above performance metrics, the current SS-GBDT is compared with various machine learning methods that have used by other researchers to solve this problem. For example, [PERSON] et al. [23] employed several ML methods such as logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), decision tree (DT) and deep learning (DL). [PERSON] et al. [24] also used ML frameworks for the problem. Deep neural networks were employed by [PERSON] and [PERSON] [25]. On the other hand, dedicated optimization algorithms such as gradient descent optimization (GDO) [26], genetic algorithm (GA) [27] and swarm optimized artificial neural networks [28] were used by other researchers. [PERSON] and [PERSON] [29] developed a fuzzy expert system for the problem. Multiple cardiac sensors for the management of heart failure (MANAGE-HF) [30] and body sensor network (BSN) [31] results are also compared. The modified self-adaptive Bayesian algorithm (MSABA), reported by [PERSON] et al. [32], is also used for comparison. The comparison of the SS-GBDT with the state of the art is reported in Table 2.  
Figure 3 illustrates the accuracy of the proposed SS-GBDT with respect the results of the state of the art. Accuracy is the by far most commonly reported metric in the literature. When compared with the conventional ML methods reported by [PERSON] et al. [23], the current SS-GBDT is found to be 11.7%, 10.2%, 11.8%, 14.7% and 12.7% better than LR, KNN, SVM, RF and DT, respectively. DL reported by [PERSON] et al. [23] is found to be on par with the current SS-GBDT, with the current method being about 0.8% better. Similarly, with respect to the ML framework reported by [PERSON] et al. [24], the SS-GBDT is 25% better. With respect to DNN [25] and MANAGE-HF [30], the SS-GBDT is 11.33% and 39% superior. However, the dedicated optimization algorithm-tuned ML results i.e., GDO [26] and swarm-optimized artificial neural networks [28], are found to have 2% and 0.78% better classification accuracy than the current SS-GBDT. Nevertheless, it is quite evident from the comprehensive comparison that the current SS-GBDT is capable of achieving very high rates of classification accuracy, which is a prerequisite in critical applications such as healthcare.