# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 2 Literature Review
### Machine Learning Models Related to Neurological Diseases
The ability of ML models to extract information from previously underutilized imaging data allows clinicians to monitor disease progression [102]. Based on fundus photographs, these algorithms are also used to identify neurological dysfunctions in PD patients. The models contribute to developing new associations that guide clinical evaluations [103; 104; 105]. SCA is studied using ML to classify its different variants. Refs. [106; 107] analyze genomic data in the diagnostic process to identify rare subtypes of SCA. Ref. [108] proposes a mathematical model for SCA37 that uses genetic variables related to repeat expansions. This paper demonstrates that ML algorithms are compatible with the issue of predicting the progression of neurodegenerative diseases. For SCA3, explainable artificial intelligence (XAI) was used to optimize the features and predict the onset of SCA [109]. In ref. [106], an unsupervised ML pipeline was applied to investigate variants of unknown significance in SCA. The result of the research highlighted the potential of ML in the clinical diagnosis of rare forms. Using MRI images, federated learning methods achieved an error rate of 13.75% in differentiating SCA2 and SCA6 genotypes [110]. Additionally, ML models are applied for the quantitative assessment of gait parameters in MS. This method is also adaptable for SCA. Ref. [111] suggests that the analysis of spatiotemporal parameters classifies the pathological forms of ataxia. HD applications are modeled through CNNs and other DL architectures that analyze medical images, such as MRI [112; 113]. These models identify distinctive patterns in neuroimaging data. The typology of ML applications models the differential diagnosis between HD and other neurodegenerative diseases, such as Alzheimer's. ML is also studied to evaluate disease progression through imaging biomarkers and structural brain changes. Besides imaging, ML applications have also been studied for voice analysis. These analyses evaluate HD symptoms through algorithms that quantify vocal markers associated with the severity of the disease [114]. Genetic and molecular biomarkers are identified through algorithms that analyze genomic datasets [115]. The metrics used to evaluate the performance of ML models concerning HD are diagnostic accuracy, symptom monitoring, and biomarker identification [116,117]. The ML models used in LBD diagnosis include NLP techniques and algorithms that analyze cognitive, demographic, and imaging data. [PERSON] et al. developed an NLP model with an AUC of 0.93 and an accuracy of 90% using conversational data, while [PERSON] et al. reported an accuracy of 95% and an AUC of 0.98 on the DementiaBank dataset [118]. [PERSON] et al. used questionnaire data to predict dementia in diverse populations, achieving AUC values of 68.6% for RF, 67.7% for LR, and 66.4% for NN [119]. The datasets include DementiaBank, UK Biobank, and data collected through online tracking tools [120,121]. The standard metrics for evaluating ML models are AUC, accuracy, sensitivity, and specificity [118,122]. Integrating cerebrovascular factors, genetic markers (apolipoprotein E--APOE), and imaging data differentiates LBD from Alzheimer's [123,124]. Table 1 presents a summary of the diseases with the dataset and the objective of the ML model. Table 1 is created based on the information extracted from the previously presented literature and highlights the importance of the dataset that must correspond to the typology of the problem to be solved. The type of dataset is always correlated with its volume. The quality of the dataset directly influences the performance of the ML model. A model's performance is evaluated using specific ML metrics. Although accuracy is one of the most commonly used metrics in the specialized literature, it is not always the most relevant--especially in the case of imbalanced datasets, where metrics such as AUC, F1-Score, sensitivity, or specificity provide a more realistic picture of performance.