# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 5 Discussion  
The results presented in this paper demonstrate the interest of researchers and the trends in using ML algorithms to diagnose and classify neurological diseases. This section discusses the involvement of these algorithms in Neuro-AI research by identifying existing gaps and proposing future study directions that encourage the evolution of the Neuro-AI field. The research included 10 types of statistical analyses in the specialized literature to evaluate the level of approach to the Neuro-ML subject rigorously. Analysis 1: The distribution of works based on authors' keywords in the Neuro-ML field generated five clusters, each representing a research direction. The central cluster highlighted the interest in Neuro-ML research for AD, PD, and MS. The other clusters highlighted the diversity of methodological approaches. Analysis 2: The distribution of works by country in the field of Neuro-ML showed that China, the USA, and India are the countries most concerned with addressing these diseases through an evolutionary technological approach. At the European level, England, Germany, and Italy stand out. Analysis 3: The distribution of works by year in Neuro-ML (2020-2024) illustrates the upward trend in the number of articles published in this field. These results confirm researchers' growing interest in applying ML in neurology. Analysis 4: The distribution of works by publishers in the field of Neuro-ML (2020-2025) indicates MDPI as the leader in Neuro-ML publications, with 647 articles, followed by Elsevier (543 articles), Springer (378 articles), FRONTIERS MEDIA SA (373 articles), and WILEY (323 articles). Analysis 5: Neuro-ML distribution by research objectives highlighted that most articles focus on predictions (1595 articles), followed by diagnosis and classification, which have similar results. Prognosis is the least explored. Analysis 6: Comparing the number of articles for ML algorithms applied in neurological research using WOS shows that SVM is the most studied model, with 597 articles, followed by ANN (525 articles), RF (457 articles), LR (378 articles), CNN (251 articles), and LSTM (142 articles). Models such as XGBoost and SVR are underrepresented. Analysis 7: The distribution of ML algorithms on specific neurological diseases has shown that Alzheimer's, Parkinson's, and MS are the most studied in the context of ML. SVM dominates in all these cases, and models such as MLP appear only in a single article about MS and LBD. The underrepresentation of rare diseases, such as ALS and prion diseases, highlights a major gap in the literature. Analysis 8: The comparison of the number of articles for ML algorithms applied in neurological research (custom procedure) showed that ANN is the most used algorithm, with 97 occurrences, followed by SVM (92), RF (63), CNN (57), and LSTM (29). Algorithms such as XGBoost and SVR appear less frequently. The customized method confirms researchers' preference for easily implementable models. There is also a noticeable lack of interest in algorithms that are more difficult to integrate. Analysis 9: The comparative analysis of the results between the customized method and WOS aimed to identify the differences in outcomes. WOS includes articles that mention terms even if they are not explicitly related to the authors' keywords, while the customized method imposes strict filters. The customized method provides more rigorous results. A disadvantage is that it excludes articles that do not meet the strict criteria. Analysis 10: The overall impact of the findings on future research highlights the importance of ML algorithms in diagnosing and classifying neurological diseases. The research identified gaps in the Neuro-ML field regarding the underrepresentation of rare diseases, the lack of addressing difficult-to-implement ML algorithms, and the absence of standardization in evaluating their performance. Exploring rare diseases and testing underutilized algorithms represent priority directions for future research.