# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 4 Results
### Overview of Publicly Available Datasets in Neuro-ML Studies  
The quality of ML models is obtained based on the quality of the dataset used for training. The quality of these data is subsequently reflected in the performance metrics used to evaluate the model's performance. For neurological diseases, especially neurodegenerative ones, datasets are used to train algorithms, which need to be generalized in diagnosis, disease progression monitoring, and biomarker identification. Datasets for neurological diseases are classified according to the type of data they consist of. Thus, there are four general types: imaging, vocal data, clinical data, and genetics. Using these datasets to train ML models allows for progress in Neuro-ML. To more easily identify the diversity and source of the datasets used in this review, Table 8 summarizes the works presented in Tables 4, 6 and 7. Table 8 presents the public datasets analyzed in the review. These datasets are used for training and validating ML models to diagnose and predict the three previously analyzed neurodegenerative diseases. Table 8 highlights that the volumes of the datasets used for medical imaging are over a thousand subjects, making them suitable for complex models associated with DL \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Disease** & **Data Type** & **Size/Composition** & **Reference** \\ \hline ADNI & AD & MRI, PET, clinical, CSF, genomics & \(\sim\)2000 participants (incl. & [126; 127; 149; 150, CN, MCI, AD) & [155] \\ PPMI & PD & BIOMERKS, clinical data & \(\sim\)1400 participants & [159; 161] \\ DementiaBank (Pitt Corpus) & AD, LBD & Speech recordings, transcripts & \(\sim\)240 individuals, transcripts & [118; 151] \\ OASIS & AD, Healthy controls & MRI & \(\sim\)1100 MRI scans & [152] \\ UK Biobank & General, AD, LBD & MRI, genetics, questionnaires & \(\sim\)40,000 MRI scans & [119; 147] \\ PhysioNet—Gait in PD & PD & Gait time-series & 306 subjects (214 PD, 92 control) & [165] \\ Kaggle PD voice dataset & PD & Acoustic features, vocal recordings & 195 instances, 23 features & [163; 167; 170] \\ Kaggle AD dataset & AD & Demographics, MMSE, MRI metadata & \(\sim\)373 instances & [152] \\ BioFIND & PD & Clinical, MRI & \(\sim\)120 participants & [159] \\ GEO (Gene Expression Omnibus) & ALS, AD & RNA-seq, microarray & Varies by dataset (GSE & [153] \\ OASIS-3 & AD & MRI, PET, clinical and cognitive scores & \(\sim\)1095 subjects, longitudinal & [155] \\ ISBI 2015 Challenge Dataset & MS & MRI, lesion masks (longitudinal) & 5 patients, 4–5 timepoints each & [183] \\ MICCAI MSSEG 2016/2021 & MS & MRI (T1, T2, FLAIR), segmentation masks & \(\sim\)15–60 patients, multiple modalities & [191] \\ Gait Analysis Dataset & MS & Accelerometry, motion & \(\sim\)20–50 subjects (varies by & \\ (custom or lab-collected) & & signals & lab) & [186; 189] \\ Transcriptomic Datasets (e.g., & & Gene expression & \\ GEO: GSE21942, GSE138266) & MS & (blood) & 30–60 samples per cohort & [184] \\ Metabolomic Dataset (not named explicitly) & MS & Serum/plasma & \(\sim\)50–100 subjects & [185] \\ NeuroVEP System Dataset & MS & metabolic markers & \(\sim\)30–100 patients (from & \\ OCB Biomarker Dataset & & signals & VEP systems) & [187] \\ OCB Biomarker Dataset & MS & CSF biomarkers (OCB & Training: \(\sim\)100, Validation: & \\ (lab-collected) & & presence) & \(\sim\)50 & [190] \\ Multi-center MRI dataset (NMO vs. MS) & MS, NMO & MRI, clinical labels & \(\sim\)100 subjects per group & [191] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative overview of datasets used in AD, PD, and MS.  
models. On the other hand, the voice and gait datasets have volumes between 200 and 300 subjects. Most MS-related datasets are small, ranging from 5 to 60 patients. There are also datasets with genomic, metabolomic, or EEG data whose size varies. The volume of the datasets is correlated with the type of data, the disease being investigated, and the kind of model analyzed. Table 8 demonstrates the diversity of datasets used in neurodegenerative studies.