# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 2 Literature Review
### Machine Learning Models Related to Neurological Diseases
The correct choice of metric is essential to avoid misinterpretations and to ensure the clinical validity of the results. There are studies in which the model's performance is compared from the perspective of the F1-Score indicator, such as the paper case [125]. It reports the performance of AD models through the F1-Score indicator. In the opinion of the authors of this review, a \begin{table}
\begin{tabular}{c c c} \hline \hline
**Disease** & **Dataset** & **Objective** \\ \hline AD & MRI, PET, DTI, clinical and cognitive data & Diagnose AD, detect subtle brain structural changes, and classify neurodegeneration patterns. \\ ALS & Genetic, proteomic, clinical datasets, neurofilament levels, metabolic biomarkers & Identify ALS biomarkers, predict disease progression, stratify patient risk, and personalize treatments. \\ FTD & MRI datasets, EEG signals, cognitive evaluations & Differentiate FTD from other dementias, classify neurodegenerative profiles, and detect anomalies. \\ HD & MRI, genetic/molecular biomarkers, vocal markers & Identify imaging biomarkers, evaluate disease progression, analyze voice symptoms, and classify HD. \\ Prison Diseases & Histopathological images, diffusion MRI data, CSF, serum biomarkers, patient-reported symptoms & Classify prion diseases and analyze cellular interactions. \\ MS & Total data, CSF, serum biomarkers, patient-reported symptoms & Classify MS severity, predict relapses, automate biomarker detection, and differentiate MS from other conditions. \\  & Vocal signals, body movement data, retinal imaging, fundus photographs, MRI & Predict PD onset, classify PD through dysphonic features, monitor disease progression, and differentiate Parkinsonian disorders. \\ SCA & Genomic data, repeat expansions, MRI images, gait parameters & Classify SCA variants, predict disease progression, differentiate SCA genotypes, and assess ataxia forms. \\ LBD & Conversational data, cognitive/demographic/imaging data, DementiaBank, UK Biobank & Diagnose LBD, differentiate LBD from Alzheimer’s, and predict dementia in diverse populations. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review summary for datasets. pertinent evaluation should include multiple indicators, such as accuracy, F1-Score, recall, precision, etc. Table 2 summarizes the research presented in this section. It presents the ML algorithm used, the column that mentions strengths with a value between 1 and 5, and the column weaknesses in numerical form. Value 1 represents significant strengths, while value 5 represents exceptional strengths. The third column, corresponding to weakness, evaluates the study's limitations. Value 1 means a minor restriction, while value 5 indicates a significant issue. The taxonomy of ML algorithms presented in Figure 2 includes the main applications represented by diagnosis, prediction, treatment, and medical recommendations. The taxonomy's structure is tree-like, with four distinct levels, each corresponding to one of the mentioned applications. Ref. [130] discusses Intellectual and Developmental Disabilities (IDDs), including Down syndrome and autism, which involve intellectual and adaptive impairments influenced by genetic and environmental factors. Advances in sequencing, imaging, and behavioral data analysis have deepened the understanding of IDDs. ML transforms IDD research by enhancing diagnosis, identifying biomarkers, and advancing personalized treatment. ML applications improve screening, early detection, and knowledge of comorbidity. Challenges remain in implementing reliable ML-based tools in clinics, requiring further innovation to bridge gaps in IDD research and care. The rising prevalence of neurodevelopmental disorders (NDDs) in children impacts health, social life, and economies [131]. Early diagnosis remains challenging, delaying interventions. ML offers promising early detection and prediction tools, integrating AI with medicine. Ref.