# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 4 Results
### Review of ML on PD
The SVM method achieved 100% accuracy for a single measurement at the metacarpal area and 82.62% in general movement positions. Ref. [173] studies ANN to differentiate PD from Parkinsonism caused by other disorders, using dopamine transporter single-photon emission computed tomography (DAT-SPECT) images. The dataset consists of abnormal DAT-SPECT images from subjects with PD and Parkinsonism. The obtained metric values have an accuracy of 86%, with a sensitivity of 81.8% and a specificity of 88.6%. Automatic monitoring of \"On\"/\"Off\" medication states in PD patients using gait signals detected by a body-worn accelerometer is proposed in ref. [174]. Four models are evaluated: RF, SVM, KNN, and Naive Bayes. The dataset used includes 20 subjects with clear motor fluctuations. The best-performing model was RF, achieving an accuracy of 96.72%, a recall of 97.35%, and a precision of 96.92%. The limitations include using data only in a laboratory environment and focusing exclusively on the \"On\"/\"Off\" states. Ref. [175] discusses the automatic assessment of postural instability in Parkinson's patients using an optimized SVM algorithm. The dataset includes 42 PD patients and seven healthy subjects. Four hundred fourteen features were extracted and selected based on Pearson correlation. The model achieved 100% accuracy in differentiating patients with mild and severe postural instability from healthy individuals. The data was obtained using the inertial sensors of a smartphone mounted on the waist. The aim is home monitoring and prevention of PD falls. Table 6 summarizes the results using each paper's objective and evaluation metrics. The papers that do not explicitly mention the models' performance metrics have been excluded from the results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Objective** & **Metrics/AI Model** & **Strengths (1–5)** & **Weaknesses (1–5)** & **Reference** \\ \hline PD diagnosis via & \begin{tabular}{c} Accuracy (95\% CI: \\ 97.82–98.5\%) \\ Severity (95\% CI: 96.3–97.3\%) \\ \end{tabular} & 5 & 3 & [156] \\ \hline \multirow{4}{*}{\begin{tabular}{c} Early PD detection via \\ spiral drawings \\ \end{tabular} } & KNN: 96.77\% (no \\  & augmentation) & & \\  & ResNet-50 + LR: 93.55\% & 5 & 3 & [157] \\  & (augmentation) & & \\ \hline PD detection via voice & \
\begin{tabular}{c} KNN: 92.2\% \\ Sensitivity: 91.1\% \\ Specificity: 93.3\% \\ \end{tabular} & 4 & 3 & [158] \\ \hline \multirow{4}{*}{
\begin{tabular}{c} PD diagnosis using \\ CNN \\ \end{tabular} } & Accuracy: 98.7\%, \\  & Sensitivity: 95.83\% & 5 & 2 & [159] \\ \cline{1-1}  & Specificity: 96.87\% & AUC: 94.5\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of PD discussed studies. The same comparative evaluation metrics as for AD were employed in the PD case. Strengths are rated based on performance metrics such as accuracy, sensitivity, specificity, and AUC. Higher values indicate better performance. Weaknesses are rated based on limitations like lower performance in certain metrics, lack of generalizability, or specific challenges in implementation.