# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 2 Literature Review
### Machine Learning Models Related to Neurological Diseases  
ML algorithms are intensively studied for the classification of neurodegenerative diseases. Ref. [43] employs an algorithm that combines Random Forest (RF), Logistic Regression (LR), and Gaussian Naive Bayes to enhance diagnostic performance on imbalanced datasets. The paper optimizes classifying neurodegenerative conditions by integrating Canny segmentation for brain scan analysis. Ref. [32] demonstrates the successful application of gait analysis using the Naive Bayes (NB) classifier for diagnosing PD. In this case, an accuracy of 96.3% was achieved. Ref. [44] uses Support Vector Machine (SVM) algorithms to distinguish PD from hereditary ataxias. Classification strategies associated with neurodegenerative conditions are adapted to specific biomarkers and imaging techniques. Ref. [45] analyzes the retinal vasculature for the classification of AD. It observes that vascular changes are correlated with disease progression. Neurodegenerative diseases exhibit various pathologies that can be classified based on unique protein aggregation profiles. In this way, specific biomarkers are recognized for high-accuracy classification [46]. Ref. [47] highlights how the spatial distribution of neuronal degeneration distinguishes between different neurodegenerative diseases. The experiments were conducted to integrate spatial data with Magnetic Resonance Imaging (MRI) and improve diagnostic capabilities. Besides value correlations, there are also approaches in ML that deal with imaging technologies. Ref. [48] examines applying deep learning (DL) methods to retinal images. This research aims to detect AD using retinal colorimetry. The results of this research are supported by ref. [49], which demonstrated that changes in retinal structures correlate with neurodegenerative conditions. These findings confirm the use of ML techniques in establishing clinical diagnoses [50]. Figure 1: Classification of ML algorithms [34; 35]. AD requires the evaluation of cognitive impairment through various biomarkers. The accuracy of diagnosing AD has been improved by integrating neuroimaging data with clinical evaluations into ML algorithms [51]. These algorithms can identify patterns associated with neuronal degeneration that are difficult to recognize through standard clinical examinations [52]. Ref. [53] uses RF and NB to analyze clinical and cognitive data to diagnose AD with the highest possible accuracy. Similarly, ref. [54] integrates MRI biomarkers in diagnosing AD. Also addressing the issue of neuroimaging signatures for AD, ref. [55] analyzes the Extreme Gradient Boosting (XGBoost) model. The Convolutional Neural Network (CNN) model detects subtle brain structural changes related to AD [56]. There is also a multimodal approach where MRI, PET, and DTI are combined in the classification issue [57]. The analysis of cellular-level changes in AD pathology is studied in [58] through ML models. ALS and FTD affect movement, cognition, and behavior. ML applications in these disorders relate to the analysis of genetic data and phenotypic variations to classify ALS subtypes or to differentiate FTD from other types of dementia. Ref. [51] mentions that the methodologies applied for Alzheimer's and Parkinson's can also be adapted for this disease. HD is combined with ML techniques in assessing genetic predisposition and tracking disease progression. And in this case, the same models can be used as in the case of AD and PD. ALS manifests through muscle weakness, atrophy, and eventually respiratory failure [59,60]. Refs. [61,62] confirms the scientific advancements in the study of specific clinical manifestations of ALS. This is due to the ability to process complex datasets such as genetic, proteomic, and clinical data. For example, refs. [63,64] have highlighted that identifying ALS-associated biomarkers facilitates patient risk stratification. The generation of biomarkers through ML approximates omics data, such as proteomics and metabolomics.