# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 3 Methodology  
Ref. [145] specifies the prevalence of AD, PD, and MS in 1990 and 2019, according to Figure 3. AD's prevalence increased by approximately 161% in 2019 compared to 1990. For PD, the prevalence increased by approximately 155%, and for MS by approximately 72%. The increase in prevalence indicates the need for modern tools equipped with AI to assist medical staff in diagnosis, prediction, treatment, and recommendations. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Diseases** & **Accuracy** & **Reference** \\ \hline AD & 90\% & [118] \\ AD & 94.71\% & [134] \\ AD & \textgreater{}80\% & [135] \\ FTD & \textasci{}79\% & [136] \\ AD/FTD & \textgreater{}90\% & [114,137] \\ ALS/SCA & AUC = 0.96 & [132] \\ ALS & 83.33\% & [133] \\ HD & 84\% & [138] \\ HD & 90.50\% & [139] \\ LBD & 75–85\% & [140] \\ PD & \textgreater{}80\% & [141] \\ PD & 80\% & [93] \\ PD & 46.56\% &  
The criteria for classifying gaps in the specialized literature have been established based on three dimensions extracted from the analysis of the results:  
* The representativeness of neurological diseases is determined by the number of articles identified through WOS queries or customized keyword analysis. If the result returned by WOS was extremely low or nonexistent, it was considered an \"underexplored disease\". The quantitative criterion was based on a limit of 5 articles published in the period 2020-2025. * The degree of utilization of ML algorithms classifies advanced algorithms as \"under-explored\" if they appear in fewer than 20 relevant studies or are not frequently used with the analyzed diseases. This quantitative criterion is based on the thresholds set by the WOS platform and customized analysis. * Standardization of evaluation metrics is necessary due to the lack of direct comparability between studies, which was identified when the analyzed articles did not report the same indicators (precision, AUC, sensitivity, specificity). This aspect complicates the cross-sectional evaluation of model performance. The methodology for assessing researchers' interest in Neuro-AI using ML is evaluated through an initial search on the WOS platform. This search included the keyword \"machine learning\" or \"ML\" abbreviation, as depicted in Figure 4. The results were exported for this search, and a map was created based on bibliographic data using the authors' keywords. The minimum number of occurrences for each keyword was set to 20. For each keyword, the co-occurrence and the number of selected keywords were calculated. The identified clusters enabled a comprehensive evaluation of the key concepts relevant to this type of analysis. Keeping the same settings, a map was also generated based on bibliographic data relating to the authors' countries of origin for co-authorship. This analysis aims to identify the countries with the highest level of interest in neurodegenerative diseases. Also, in this category of statistics, the distribution of research by year and the distribution of researchers by publications were identified. The two statistics highlight the interest in Neuro-ML and the publications encouraging such research. Figure 3: Prevalence of neurological disorders in 1990 and 2019. Statistics were generated based on the NeuroAIStats.xlsx file (available in the Supplementary Materials section) regarding the most used ML algorithms in the neural domain for the four objectives specific to this AI component. Subsequently, statistics were generated for each ML algorithm from a total of 46 identified. The algorithms that provided over 100 results in WOS were extracted. In the end, statistics were generated for the neural diseases presented in the literature review section in relation to each previously selected ML algorithm. Subsequently, a filtered search was conducted by including the terms \"diagnosis\", \"prediction\", \"classification\", and \"prognosis\". These words are associated with ML algorithms' objectives in Neuro-AI, which involve pattern recognition. Next, a list of 46 algorithms specific to ML models was created.