# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 3 Methodology
Then, for each algorithm, a new set of searches was conducted that included the name of the ML algorithm, the name of the algorithm itself, and \"neuro AI\", as shown in Figure 4. The search aims to identify the field's most extensively studied ML algorithms. The previous WOS search results identified the algorithms that yielded over 100 articles in the Neuro-AI field. All identified algorithms were combined to construct WOS queries, with each neurodegenerative disease identified in the literature aiming to identify research gaps related to specific algorithms. Figure 4 presents the logical diagram used in this category, which focuses on the statistical evaluation of Neuro-AI research related to ML algorithms in WOS. Figure 4: Flow procedure employed in evaluating the number of articles in different scenarios. The procedure presented in Figure 4 generates two files, based on which detailed analyses will be conducted. The Author_keywords.xlsx file was generated from the WOS platform via export, and the NeuroAIStats.xlsx file was built using the execution of WOS queries. Both files, along with the article, are publicly available for analysis. The second type of analysis included extracting all the results generated by the initial search in WOS from the Author_keywords.xlsx file. For all the results, the author's keywords were extracted. Subsequently, a program was implemented in C#, version 7.3 that searched all previously evaluated ML algorithms individually, using the authors' keywords. In this way, the frequency of occurrence of each algorithm was constructed within the general search that included the term \"ML.\"  
Figure 5 illustrates the workflow used to compute the authors' keyword frequency. The results are saved in Sheet 4 of the NeuroAIStats.xlsx file. In this approach, the algorithms effectively addressed in the specialized paper were identified compared to the results provided by the WOS-specific search. Summarizing the presented methodology, 10 analyses are identified and explored in detail:  
* Analysis 1: The distribution of papers based on the authors' keywords in Neuro-ML aims to identify the main themes based on research clusters in the field of Neuro-ML. * Analysis 2: The distribution of papers by country in the Neuro-ML field evaluates the level of interest grouped by country for research in the field of neurological diseases. * Analysis 3: The distribution of papers by year in the Neuro-ML field (2020-2024) analyzes the temporal trends of researchers' interest in applying ML in neurology. * Analysis 4: The distribution of works by publishers in the Neuro-ML field aims to identify the publishers that facilitate disseminating research results in the Neuro-ML field. * Analysis 5: The Neuro-ML Distribution by Research Objectives aims to evaluate researchers' priorities based on specific objectives, such as prediction, diagnosis, and classification. Figure 5: Flow procedure to evaluate the appearance frequency of each algorithm in the research papers (2020–2025). Analysis 6: Comparing the number of articles for ML algorithms applied in neurological research (WOS) aims to establish the most used ML algorithms in neurological research. * Analysis 7: Comparative metrics between ML models applied in different diseases research. * Analysis 8: Comparing the number of articles on ML algorithms applied in neurological research (custom procedure) obtains a complementary WOS perspective on using ML algorithms through customized methods. * Analysis 9: The comparative analysis of the results between the customized method and WOS aims to compare the results obtained through two different approaches to identify the differences. * Analysis 10: Identifying gaps and opportunities in the specialized literature aims to highlight future research directions, focusing on the underrepresentation of rare diseases and the exploration of underutilized algorithms. The results obtained for the 10 proposed analyses are presented in the following section.