# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 4 Results
### Distribution of ML Algorithms Across Specific Neurological Diseases Relevant in Research  
This distribution will be further used to identify researchers' interest in studying these algorithms in certain neurological diseases. After all the WOS queries were run and the results were centralized in Sheet 3 of the NeuroAIStats.xlsx file, the results that provided values between 1 and 5 were excluded, as they were considered irrelevant. The remaining results, after filtering, showed exceptionally valuable information. Figure 12 shows that the most studied diseases in the context of ML are Alzheimer's, Parkinson's, and MS.  
The results in Figure 12 show that SVM (pink color) is the most used model, with 46 articles for PD, 37 for AD, and 9 for MS. RF (orange color) follows with 48 articles for PD, 31 for AD, 7 for MS, and 6 for ALS. CNN (light green color) reports 34 articles in the context of AD, 42 for PD, and 7 for MS, with a preference for medical image analysis. KNN (lavender color) stands out with 23 articles for PD and 12 for AD, suitable for classification problems. DT (yellow color) has 24 articles for PD and 7 for AD. Other algorithms, such as XGBoost (turquoise color), ANN (burgundy color), LSTM (dark blue color), MLP (gray color), etc., have rarely been studied and have few or no results. For example, MLP appears with only 1 article for MS and LBD. Rare diseases, such as ALS and prion diseases, show low or zero values for most models. This aspect highlights a research gap. GNB, although rarely used, appears in 7 articles about PD.  
Figure 11: Distribution of research papers in Neuro-ML by the ML algorithm employed (2020–2025).  
The value of these results indicates the following gaps in the literature:  
* The reduced volume of datasets required for the other ML algorithms generated a low number of results;
* The algorithms are not suitable for the issues of neural diseases;
* The lack of popularity of these algorithms causes researchers' interest in them to be low.  
Figure 13 shows that AD, PD, and MS have generated a consistent number of articles in the specialized literature, even though their number is still relatively small compared to the large number of cases existing globally. The results presented in Figure 13 are obtained through WOS queries, including the search terms \"ML\" or \"Machine Learning\", the disease name, and the year constraint.  
As shown in Figure 13, very few articles appear for other diseases, such as ALS, FTD, and so on, compared to the other three diseases identified as most investigated.  
Figure 12: Distribution of ML algorithms across specific neurological diseases relevant in research (2020–2025).  
Figure 13: Distribution of research papers in the Neuro-ML by disease (2020–2025).  
Comparison of Article Counts for ML Algorithms Applied in Neurological Research Based on a Custom-Made Procedure  
The results presented in Figure 14 reflect the frequency with which each of the 46 evaluated algorithms is mentioned in the keywords of authors in the field of neurology. This customized statistic was created for comparative analysis, and WOS returned the results based on the queries constructed for each algorithm. Figure 14 shows that ANN is the most used algorithm, with 97 occurrences. This is followed by SVM, 92 occurrences, and RF, 63 occurrences. CNN appears 57 times, followed by RF (63), LSTM (29), DT (27), XGBoost (24), SVR (22), and LR (20). Algorithms with fewer than 20 results are excluded from the analysis, as the WOS analysis was configured with the same occurrence threshold of 20.