# Comparison of Dimensionality Reduction Approaches and Logistic Regression for ECG Classification
## 5 Discussion and Conclusions  
This study evaluated the performance of interpretable machine learning models for the classification of five clinically relevant cardiac rhythms--SB, SR, AFIB, ST, and SVT--using engineered features from the SPH ECG dataset. We focused on classical approaches such as K-Nearest Neighbors (KNNs) and logistic regression, both in frequentist and Bayesian frameworks, combined with dimensionality reduction techniques including Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). The results demonstrate that the UMAP-KNN model yielded the most balanced and robust performance across diverse rhythm types. It showed particular effectiveness in handling difficult and imbalanced classes such as SR, AFIB, and ST, achieving F1-scores of 63.26%, 45.95%, and 73.65%, respectively. By capturing nonlinear relationships in the feature space, UMAP improved the separability of rhythm classes and enhanced the performance of the KNN classifier. In contrast, logistic regression models, especially the Bayesian variants (LR-B) and LR-BN), performed better in linearly separable classes such as SB and SVT, with F1-scores exceeding 91%. However, they struggled in overlapping or under-represented classes, which highlights the limitations of linear models in complex classification tasks. Compared to recent deep learning approaches applied to the same dataset [19; 20; 21; 22], our models achieved more moderate performance levels. Nonetheless, they offer significant advantages in terms of interpretability, computational efficiency, and ease of implementation. Logistic regression provides transparent coefficient interpretation,  
Figure 5: F1-score per diagnostic class and classification model. while Bayesian models quantify predictive uncertainty, a key aspect in clinical decision-making. The use of dimensionality reduction, particularly UMAP, also adds interpretability by enabling the visual exploration of the data structure, an asset often lacking in deep neural networks. In contrast, our approach offers a favorable trade-off between explainability, computational efficiency, and classification performance, especially in clinical decision support systems where transparency is essential. Unlike end-to-end black-box models, our approach explicitly separates feature extraction and classification stages, allowing for diagnostic insight into both dimensionality structure and classification boundaries. This separation enhances transparency and supports clinical interpretability, features that are often lacking in deep learning pipelines. This work, however, presents certain limitations. First, only pre-engineered features were used, excluding raw ECG waveform data which could carry additional diagnostic information. Second, although stratified data splitting was employed, class imbalance persisted, affecting sensitivity and precision in minority classes. Third, no advanced tuning or ensemble techniques were applied, which may have limited the full potential of the tested models. To address these limitations, future research could explore hybrid approaches that combine deep representation learning with interpretable classification layers, such as CNN-based feature extraction followed by logistic regression. Attention mechanisms or rule-based modules could further enhance transparency. Including raw ECG signals may improve classification fidelity, and class imbalance could be addressed using resampling techniques or specialized loss functions. Moreover, for real-time clinical applications, deploying compressed or quantized versions of these models could ensure compatibility with constrained hardware environments. In conclusion, this study shows that interpretable, low-complexity models--especially UMAP-KNN--can provide a competitive and clinically meaningful performance for ECG rhythm classification.