# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 4 Results
### Review of ML on PD
Bayesian Optimization-SVM (BO-SVM) represents a model for classifying PD patients introduced in ref. [163]. The model optimizes hyperparameters for six ML models: SVM, RF, LR, NB, Ridge Classifier, and DT. The dataset uses 195 instances and 23 features to identify whether a person has PD or not. SVM achieved the best results with an accuracy of 92.3%, using Bayesian Optimization. A dynamic system combined with wearable sensors and ML algorithms to detect balance anomalies in PD patients is presented in ref. [164]. The dataset uses kinematic measurements recorded by inertial sensors on 20 PD patients and 15 healthy subjects. Fifty-two classifiers from algorithm families such as DT, KNN, SVM, and ANN were evaluated. From the investigations, the best algorithm was Fine K-Nearest Neighbor (FKNN), with an accuracy of 95.6%, a recall of 99%, and a precision of 95.2%. Ref. [165] discusses building a classifier to distinguish between Parkinson's patients and healthy individuals, using gait data collected from subjects. The evaluated models include LR, SVM, DT, and KNN. The dataset is \"Gait in Parkinson's Disease\" from PhysioNet, which contains data from 92 healthy subjects and 214 patients with idiopathic PD. The size of the dataset is 306 subjects. The metric values obtained from the test were 92.3% for accuracy and 0.929 for AUC. A predictive model for cognitive degeneration in PD patients, using ML methods, is introduced in ref. [166]. The study evaluated cognitive classification models using SVM and PCA. The dataset contains clinical data, plasma biomarkers, and neuropsychological test results. The PCA-SVM model with 32 parameters achieved an accuracy of 92.3% and an AUC of 0.929, while with 13 selected features, the accuracy was 100%, and the AUC was 1.0. Ref. [167] discusses the identification of PD based on voice disorders using a dataset from Kaggle. It evaluates comparative techniques and ML models, including an unsupervised autoencoder for feature selection and a 1D CNN for PD classification. Obtained metrics are CNN-1D with F\({}_{1}\)-Score at 92.7% and LR with F\({}_{1}\)-Score at 92.2%. Limitations include that only one dataset is analyzed without methods for balancing the dataset. The study suggests that CNN-1D offers superior performance compared to manual selection techniques [168]. Ref. [129] studies postural stability in patients with PD using a new methodology employing the empirical mode decomposition (EMD) method. The evaluation is carried out using ML models: KNN, DT, RF, and SVM. The dataset includes 60 subjects (28 healthy and 32 with PD). SVM performs 92%, while the Dempster-Shafer method has an accuracy of 96.51%. This methodology helps differentiate healthy individuals from those with PD, achieving correct classification rates of up to 96%. The models evaluated in ref. [169] include classifiers such as KNN, SVM, and DT. The methods used achieved an accuracy of 95.4%, a sensitivity of 94.9%, a specificity of 93%, a precision of 95.2%, and an F\({}_{1}\)-Score of 95.5%. The early detection of PD is evaluated in ref. [170] using KNN, MLP, and SVM. The dataset is imbalanced, with more uninfected patients than infected ones. Synthetic minority over-sampling technique (SMOTE), RFE, and PCA were used to address the imbalance. The obtained values include a maximum SVM accuracy of 98.2% and a maximum KNN specificity of 99%. The potential of using voice for PD screening in resource-limited settings is investigated in ref. [171]. The dataset includes 11,942 voice recordings from 1078 participants with PD and 5453 control participants, from which 3000 balanced samples were selected for training and testing, using 10-fold cross-validation, and the remainder (8942 samples) for model validation. The obtained metrics are a sensitivity of 67.43% and a specificity of 67.25%. Ref. [172] addresses the discrimination between essential tremors and Parkinsonian tremors. The dataset used includes measurements of lower arm tremors collected from patients with different types of tremors and healthy volunteers.