# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 4 Results
### Review of ML on PD  
Ref. [156] studies PD to the Dense Multiscale Sample Entropy (DM-SamEn) method for feature extraction from vertical ground reaction force (VGRF) signals. The evaluation involves three classifiers: KNN, RBFN-SVM, and MLP. The analyzed data are ground reaction force signals from patients with PD. The metrics obtained are PD diagnostic accuracy (95% CI: 97.82-98.5%) and severity assessment (95% CI: 96.3-97.3%). Limitations include the lack of external validation, limited interpretability, and data imbalance. Ref. [157] is about the early detection of PD by analyzing spiral drawings. The evaluation focuses on hybrid models: KNN, CNN with SVM, and ResNet-50 with LR. The study uses a dataset of spiral drawings. KNN had an accuracy of 96.77% without augmentation but decreased with augmentation. ResNet-50 with LR achieved 93.55% accuracy with augmentation through rotation and flipping. Ref. [158] analyzes PD detection based on voice changes, focusing on phonation, articulation, and prosody. PD diagnostic models were evaluated using speech signals and SVM and KNN methods. The data comes from voice recordings collected at the Medical University of Warsaw. The sequential floating forward (SFFS) method was used for descriptor selection. In 10-fold cross-validation, the KNN method achieved a recognition rate of 92.2%, with a sensitivity of 91.1% and a specificity of 93.3%. The diagnosis of PD uses CNN in ref. [159]. This evaluates the CNN model in comparison to other ML techniques (NB, DF, SVM, and ANN). The dataset used is from the Parkinson's Progression Markers Initiative (PPMI) and includes T2-weighted MRI images of patients with PD and healthy controls. The evaluation metrics include accuracy (98.7%), sensitivity (95.83%), specificity (96.87%), and the AUC of 94.5%. Ref. [160] discusses the development of a model for the early detection of PD using human voices. A transformer-based model for dysphonia measurements is evaluated and compared to existing models, including Gradient-Boosted Decision Trees (GBDTs), MLP, SVM, and RF. The dataset uses tabular vocal measurements. The proposed model improves AUC by at least 1% compared to state-of-the-art. Ref. [161] discusses dynamic brain activity in PD, focusing on dynamic regional homogeneity (dReHo). It utilizes a dataset comprising 57 patients with PD and 31 healthy controls (HCs), including resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans and neuropsychological examinations. Compare regional homogeneity (ReHo) and dReHo between groups and analyze the associations between dReHo and clinical and neuropsychological measures. Evaluator models use SVM to differentiate between groups. The metric values include the correct identification of 98% of individuals by SVM (\(p<0.001\)). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Strengths (1–5)** & **Weaknesses (1–5)** & **Reference** \\ \hline Multi-stage diagnosis of AD & 5 & 2 & [126] \\ Differences in progression from MCI to Alzheimer’s & 5 & 3 & [148] \\ ML for AD diagnosis using MRI & 5 & 3 & [149] \\ Detecting AD using PET and MRI images & 5 & 3 & [150] \\ CAD for detecting AD stages & 5 & 3 & [127] \\ Evaluating dual-phase FBB vs. delay-phase FBB & 4 & 3 & [151] \\ Ontology for AD diagnosis & 4 & 3 & [152] \\ Untargeted metabolomic profiling for MCI dementia conversion & 4 & 3 & [154] \\ Evaluating RF compared to other ML models & 5 & 3 & [155] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Strengths and weaknesses of AD investigated articles in the literature. The conclusion highlights abnormal brain activity in the precuneus of PD patients and its associations with neuropsychiatric symptoms. A novel aspect is presented in ref. [162], which highlights feature selection techniques for improving model metrics. Thus, the performance for XGBoost and DNN increased by 10% and 38.18%, respectively, while SVM and DT increased by 0.91% and 7.27% after applying the feature selection methods.