## 5 Discussion
 & SVM Accuracy: \textasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciitifasciasciitifasciitifasciitifasciasciitifasciitifasciasciitifasciitifasciasciitifasciitifasciasciitifasciasciitifasciasciitifasciasciitifasciasciitifasciasciitifasciasciasciitifasciasciitifasciasciasciitifasciasciasciitifasciasciasciitifasciasciasciitifasciasciasciitifasciasciasciitifasciasciasciasciitifasciWhile SoH estimation is typically approached as a regression problem in the literature for the NASA battery dataset, this study treats it as a classification problem, offering a different perspective on battery health estimation. Regression methods estimate the SoH as a continuous variable, providing precise numerical values. Conversely, classification approaches discretize this continuous spectrum into distinct categories or classes. By categorizing SoH into discrete levels, classification facilitates a more intuitive interpretation of battery performance, aligning it with specific operational or performance thresholds. This categorical approach can enhance decision-making processes in certain applications by prioritizing interpretability and expediting actions based on broader performance categories rather than granular values. Moreover, classification methods often yield more robust and stable results within defined ranges, as they focus on distinguishing between broader categories rather than striving for precise predictions of continuous SoH values, which may be subject to noise or measurement inaccuracies. For instance, as shown in Table 10 of Ref. [52], SoH was categorized into three discrete classes. However, since this analysis utilized the Oxford dataset, it was not directly compared in the discussion section, to ensure consistency in the comparative evaluation. Ref. [53] utilized a two-class SoH classification approach with an optimized SVM model on the NASA dataset, reporting an accuracy of 90.48%, recall of 77.47%, and precision of 100.00%. In contrast, the proposed method demonstrates significant improvements by incorporating both feature extraction and optimization processes. Specifically, the use of 15 TD features, later refined to 10 TD features using RFI, combined with LSTM, yielded an accuracy of 99.06%, recall of 99.13%, and precision of 98.54%. Figure 5: Boxplot comparison of SVM and LSTM performance metrics for NASA battery dataset. In ref. [54], a two-class SoH classification approach was implemented using an LSTM model on a proprietary dataset. The study reported an accuracy of 88%, recall of 91%, and precision of 95%. This demonstrates the capability of LSTM models to effectively capture time-series dependencies and deliver high performance in battery health classification tasks. The use of a proprietary dataset highlights the importance of tailored data collection and preprocessing in achieving robust and reliable results for SoH classification. These findings further validate the utility of Deep Learning techniques, such as LSTM, in advancing battery management systems by providing accurate and actionable insights. These findings underscore the effectiveness of the proposed methodology, which leverages feature optimization to enhance performance while maintaining computational efficiency. From the perspective of computational complexity, reducing the number of input features from 15 to 10 across both datasets significantly decreases the dimensionality of the LSTM input matrix. This reduction directly alleviates the computational burden by minimizing the number of matrix multiplications and weight update operations--components that are among the most computationally demanding during the training process. Moreover, the streamlined feature space results in a lower kernel computation overhead, thereby improving the overall efficiency of the model.