# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 4 Results
### Review of ML on MS  
One of the main research directions in MS focuses on ML algorithms. Ref. [176] demonstrated that models using KNN-type algorithms have superior accuracy in differentiating MS lesions in MRI scans compared to traditional approaches. Refs. [85; 177] predict the likelihood of disease progression and exacerbations based on neuroimaging characteristics. Ref. [178] presents a review highlighting the techniques used in identifying patterns in large datasets to assist clinicians in making informed decisions regarding managing patients' diseases. Additionally, DL has enabled the automatic extraction of features from complex datasets, as in the case of ref. [179]. This uses CNN to learn hierarchical features that facilitate diagnosis with superior accuracy compared to traditional methods in multiple sclerosis. Ref. [180] improves the predicted values compared to conventional methods, while ref. [181] investigates integrating multimodal data such as imaging, clinical assessments, and biological markers in identifying MS.  
Another direction in which studies have explored MS involves unsupervised learning to classify MS patients based on clinical and MRI data without predefined labels. This approach can lead to personalized treatment strategies. Unsupervised techniques help discover subtypes within the heterogeneous population of MS patients. The direct consequence concerns adapting the therapeutic plan for each patient. Recent models that predict future clinical outcomes are being investigated through metrics such as gait analysis and offer results that promise advancements in early disease identification. Ref. [182] proposes an ensemble of dormant models from LSTM, DNN, and CNN optimized by the improved walrus optimization algorithm. For detecting MS lesions using MRI images, a CNN implemented with the VGG16 architecture achieved results associated with accuracy metrics of 98.44%, specificity 99.4%, and sensitivity 97.56% [183]. Transcriptomic-based approaches have shown that blood transcriptomic signatures can classify MS with an accuracy of 97% [184]. The analysis of metabolomic data using ANN achieved an accuracy of 87% and a sensitivity of 82.5%, and a specificity of 89% [185]. Regarding gait analysis, a ResNet model with regression-based normalization achieved accuracy and an F\({}_{1}\)-Score of 100% when generalized from simple walking to walking while talking [186]. For the evaluation of visual signs, the NeuroVEP system agreed with standard tests in 91% of cases [187]. Automatic detection of the central vein sign in MS lesions using a 3D convolutional neural network (CVSnet) achieved an accuracy of 91% on the test set [188]. Accelerometry-based classification achieved 87% accuracy compared to EMG [189]. ML models for biomarkers identified specific panels for the presence of oligoclonal bands (OCB) in CSF: the MS OCB+ model achieved 91% sensitivity and 94% specificity in training and 81% and 94% in validation, respectively; the MS OCB- model performed worse [190]. SVM and KNN applied to MRI differentiated MS from Neuromyelitis Optica (NMO) with precision 98%, recall 99%, F\({}_{1}\)-Score 99%, and accuracy 99% [191]. A hidden layer ANN predicted the MS diagnosis with a testing accuracy of 75% [192]. Table 7 synthesizes the MS discussed studies and outlines the strengths and weaknesses of the method's performance employed. The scores for strengths are assigned based on the method's performance in terms of accuracy, sensitivity, specificity, and clinical applicability. The scores for weaknesses reflect the severity of the limitations, such as poorer performance in certain metrics, lack of generalizability, or specific implementation challenges.