# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 5 Discussion
### Distribution of ML Algorithms in the Context of Specific Neurological Diseases  
The most studied neurological diseases in ML are AD, PD, and MS. For AD, the most suitable algorithm is SVM (37 articles), followed by RF (31 articles), CNN (34 articles), KNN (12 articles), and XGBoost (7 articles). Regarding PD, the most suitable model is again SVM (46 articles), followed by RF (48 articles), CNN (42 articles), KNN (23 articles), and XGBoost (18 articles). Regarding MS, the most studied model is SVM (9), but RF and CNN are hardly studied in this context. The results show that Alzheimer's and PDs, which are common diseases, are better represented in the specialized literature. Rare diseases, such as ALS and prion diseases, are poorly studied in the literature. This observation indicates a gap in exploring these algorithms in the context of rare diseases. The frequency of occurrence of ML algorithms in the authors' keywords was analyzed through the WOS platform and a customized method proposed by the authors of the present article. The customized method showed that the preferred ML algorithm is ANN. It has 97 occurrences, SVM has 92, and RF has 63. Algorithms such as XGBoost and SVR are underrepresented in the authors' keywords regarding frequency of occurrence. Regarding the comparative analysis of the results obtained through keyword identification in the customized method vs. WOS, the results show a series of differences that reveal a shortcoming in the literature. The WOS method counts articles that mention terms in a broader sense. Therefore, the authors' keywords are not treated restrictively to the term algorithm. Instead, the customized method applies strict filters. Thus, only articles that mention the algorithm in the authors' keywords are quantified. The proposal for quantification through the customized method provides a rigorous picture of the authors' preferences in addressing certain ML models related to specific diseases. A detailed analysis of metric performance in literature studies was reported on the two diseases considered the most studied in the literature, AD and PD. Regarding AD, the best accuracy identified in the literature was 97.46%, with an F\({}_{1}\)-Score of 93.19%, recall of 95.79%, and precision of 94.62% in ref. [149]. For Parkinson's, the highest accuracy was 98.2%, as reported in ref. [170]. In addition to what has been presented, the authors emphasize that the clinical applicability of ML models in neurology has been discussed indirectly by focusing on diagnostic accuracy and disease progression prediction:  
* ML models can shorten the diagnostic time and reduce the error rate in the early identification of neurodegenerative diseases (e.g., early diagnosis in Alzheimer's with 97.46% accuracy using DBN-MOA). * Algorithms allow personalized treatment by stratifying patients based on genetic or imaging biomarkers (e.g., in ALS or MS). * The integration of ML into clinical practice remains dependent on standardized protocols and validation on large clinical datasets, which is recognized as a limitation and a priority direction for the future. For an efficient clinical application, the authors recommend developing standardized validation procedures and expanding research to rare diseases where the impact of early diagnosis can be significant. The ANN algorithm is the second most frequently used in the Neuro-ML field. Figure 11 shows that the total number of articles using a specific algorithm in the Neuro-ML field aims at the unified treatment of all neurological diseases. Figure 12 highlights the frequency of use of each algorithm based on the neurological disease. Therefore, although ANN is frequently mentioned in the general literature on Neuro-ML, it does not dominate in AD, PD, and PD disease studies. Figure 12 exclusively highlights the three neurodegenerative diseases. The difference between general use and applicability to specific conditions is not a contradiction but highlights an uneven distribution.