# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 2 Literature Review
### Machine Learning Models Related to Neurological Diseases
Through these techniques, the accuracy of diagnosis is improved [63,65]. Predictive ML models identify major variations in biomarkers throughout the course of the disease. This ability leads to faster interventions [63,66]. The application of ML in ALS integrates multiple clinical parameters for staging disease progression. For example, models have been proposed to evaluate the effects of neurofilament levels and metabolic biomarkers on disease trajectories [66,67]. This approach is the path to personalized treatments that correlate the individual needs of the patient with the stage of the disease and the biomarker profile [67, 68, 69]. Prion diseases, associated with neurodegeneration and infectivity, use ML applications for diagnosis. Ref. [70] used CNN for the histopathological classification of these diseases, while ref. [71] integrated diffusion MRI (dMRI) with ML also for diagnosis. Ref. [72] has shown the importance of microglia in modulating progression through the analysis of cellular interactions with ML models. FTD is characterized by behavioral and linguistic alterations in individuals under 65 years. Diagnosing FTD is difficult due to clinical overlaps with other types of dementia or psychiatric disorders. This aspect leads to diagnostic errors [73, 74, 75]. For this reason, integrating ML techniques represents an alternative to reducing errors. They use MRI datasets, cognitive assessments, and electroencephalograms (EEGs) [76, 77, 78, 79]. For example, MRI-based models analyze brain regions affected by atrophy. The objective of ML models is to classify the type of disease based on neurodegenerative profiles [78, 80, 81]. Refs. [82, 83] propose EEG as a real-time tool for obtaining anomalies in brain activities characteristic of FTD. MS is also studied through ML. The models analyze MRI data and patient-reported symptoms to classify the severity of the disease [50]. ML models are also suitable for predicting potential relapses [53]. MS is a disease that manifests unpredictably, which complicates traditional diagnostic approaches. ML models can solve this problem by leveraging complex datasets that include neurological imaging and biomarker analysis [84; 85]. MRI analysis is a time-consuming process prone to human errors [86]. Refs. [87; 88] have demonstrated that CNNs and RF can automate the detection of MS-associated biomarkers from neurological imaging and blood samples. For example, ref. [89] demonstrated the ability of CNNs to differentiate MS from other neurological conditions through advanced quantitative MRI analysis. The data used by the models correspond to cerebrospinal fluid (CSF) and serum biomarkers [85; 87]. The identification of certain cytokine patterns associated with MS was facilitated by ML techniques [90; 91]. PD is researched in ref. [17] to analyze vocal signals through ML models. Similarly, ref. [92] analyzes body movement, while ref. [93] examines multimodal stages to predict their progression. Refs. [94; 95] report improvements in prediction through SVM models and ensemble learning. Refs. [96; 97] mention that the integration of wearable devices improves prediction by generating data collected from the wearer. The research results presented in ref. [98] managed to predict both the prevalent and incident forms of PD. The predictive accuracy in images was 71%. This finding is also supported by ref. [17], through PD prediction in retinal imaging and enhanced vocal signals. SVM and RF algorithms are used to classify PD through dysphonic features [98]. Besides imaging techniques, voice signal analysis represents another method for early detection of PD. Advances in this field have led to a classification accuracy of over 90% in spectrogram analysis [99]. Ref. [100] reports that ML algorithms surpass human performance in diagnosing PD. This is possible by analyzing MRI characteristics that indicate neurodegenerative changes associated with the disease. Metabolic imaging uses ML to differentiate Parkinsonian disorders based on metabolic indices derived from imaging [101].