## 3 Results
### ML Methods
#### 3.1.1 Purpose-Based Classification  
1. Diagnostic Differentiation  
NPH vs. Healthy Controls  
In a study by employed an SVM to detect NPH patterns in MRI scans, achieving an impressive accuracy of 0.93 and an ROC-AUC of 0.99 [38]. These metrics represent exceptional discriminative capability that approaches the performance of specialized neuroradiologists, highlighting the potential of ML algorithms to serve as reliable diagnostic support tools for NPH detection. The algorithm demonstrated the highest discriminative power in specific brain regions, including periventricular regions, lateral ventricles, and Sylvian fissures--areas known to be crucial for NPH diagnosis in clinical practice. This alignment between the algorithm's focus and established radiological markers validates the clinical relevance of the approach.  
Their results suggest that MRI morphometric and advanced image processing may effectively capture hallmarks of NPH useful for developing AI systems, especially when integrated into a multiparametric approach. However, the complexity of NPH means that simply using common measures and segmentation algorithms may be insufficient; determining optimal diagnostic signatures will require systematically investigating which feature combinations provide the greatest discriminatory power.  
In the same study the results suggest MRI morphometric and advanced image processing may capture hallmarks of NPH useful for developing AI, especially when fused into a multiparametric approach [38]. But the complexity of NPH means simply using common measures and segmentation algorithms may be insufficient; determining optimal signatures for diagnosis and monitoring will require systematically investigating which features, in which combinations, provide the greatest discriminatory power.  
In another study developed multiple ML models to explore the effectiveness of commonly used morphological parameters for NPH diagnosis [37]. Their feature importance analysis revealed the most weighted diagnostic features were Evans ratio (24.79%), frontal horns' length (10.64%), and disproportionately enlarged subarachnoid space hydrocephalus--quantifying for the first time the relative importance of these clinical markers. When comparing model performance, the artificial neural network achieved the highest ROC-AUC of 0.96 \(\pm\) 0.05, marginally outperforming random forest (0.96 \(\pm\) 0.06), SVM (0.94 \(\pm\) 0.05), and XGBoost (0.94 \(\pm\) 0.07). The minimal performance differences between these diverse algorithms (all within 2 percentage points) suggests that feature selection may be more critical than algorithm choice for accurate NPH diagnosis.  
Another team implemented a multinomial LogReg algorithm to predict probabilities across three different diagnoses: healthy elderly controls, NPH, and Alzheimer's disease (AD) [32]. This approach represents an important advance over binary classifiers, addressing the clinical reality where differential diagnosis across multiple conditions is often required. Their model demonstrated exceptional classification accuracy of 96.3%, with only two misclassifications out of 15 NPH cases. This impressive performance indicates that even relatively simple ML algorithms, when provided with appropriate features, can effectively differentiate between clinically similar neurodegenerative conditions. The model's ability to provide confidence values for each case (averaging 97% for NPH diagnoses) offers an additional dimension valuable for clinical decision support. The high sensitivity (100%) and specificity (87%) in the binary LogReg model further confirm the robustness of this approach for NPH-AD differentiation.