## 4 Discussion
As shown in Figure 3, hybrid methods consistently achieved the highest balanced performance across all metrics, suggesting this integrated approach represents the most promising direction for future research. \"Our recommendation for hybrid ML-DL frameworks can be further contextualized within the broader field of AI-driven complex system management. Reference [52] propose AI-driven networks for accident prevention in complex systems that bear striking similarities to the challenges of NPH diagnosis. Their approach emphasizes the integration of heterogeneous data streams through layered AI architectures that combine traditional statistical methods with deep learning to achieve more reliable accident prediction. Similarly, in NPH diagnosis, the integration of multiple data modalities (imaging, clinical, and functional) through hybrid frameworks can create a more comprehensive diagnostic system. Just as [PERSON]'s complex network approach connects seemingly disparate risk factors to improve accident prediction, our proposed hybrid framework could better capture the complex interrelationships between ventricular morphology, clinical symptoms, and functional measures to enhance diagnostic reliability. This systems-level perspective underscores the value of hybrid approaches that can model both explicit domain knowledge (through traditional ML) and implicit patterns (through DL) for more robust NPH detection\". Feature analysis techniques seek to identify the most predictive measurements for NPH. While they can highlight NPH-specific features, the identified metrics may also be present in other conditions. These approaches can also be computationally intensive. Finally, ML prediction models aim to provide risk scores and forecasts for NPH patients. However, their predictions may not be accurate for all individuals. In summary, these techniques have parallel strengths and weaknesses. While achieving high performance, they vary in their interpretability, transparency, data dependence, and accuracy for individual patients. Future research should evaluate how combining these methods in an integrated framework could maximize their benefits while mitigating limitations. The significant variability in model performance based on feature selection highlights a critical challenge in NPH diagnosis. When developing ML models for NPH, researchers must address the inherent data imbalance, as NPH patients typically represent a smaller class compared to control groups or other conditions. Drawing from risk assessment frameworks in other domains, techniques like SMOTE oversampling could substantially improve model generalizability. As demonstrated by [53] in the rockburst risk assessment, SMOTE successfully addresses class imbalance by generating synthetic samples of the minority class, leading to a more robust classification under a GBDT framework. Applied to NPH diagnosis, such techniques could help models learn more effectively from limited NPH cases, particularly when distinguishing NPH from more prevalent conditions like AD. Furthermore, the integration of multiple algorithms under a unified framework, as demonstrated in the risk assessment literature, suggests that ensemble approachesmight better handle the feature relevance and data heterogeneity challenges inherent in NPH diagnosis. Our review has several methodological limitations. First, despite our comprehensive search strategy, we may have missed relevant studies, particularly those published in non-indexed journals or in languages other than English. Second, our assessment of the risk of bias was hampered by incomplete reporting in many of the included studies, particularly regarding patient selection and reference standards. Third, the rapid evolution of AI techniques means that newer approaches may not be adequately represented in our review. Finally, our inability to conduct a meta-analysis due to heterogeneity limits our ability to provide precise estimates of diagnostic accuracy for different AI approaches.