# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 4 Results
### Review of ML on AD  
Ref. [126] analyzes multi-stage diagnosis of AD using two types of biomarkers: blood gene expression and clinical data, both obtained from the AD Neuroimaging Initiative (ADNI). The genomic expression dataset includes 744 samples, while the clinical dataset includes 2000 samples. The evaluated models include DL, SVM, gradient boosting (GB), and RF. The maximum receiver operating characteristic (ROC) area under the curve (AUC) metric values for clinical data are 0.989 (cognitively normal--CogN), 0.927 (mild cognitive impairment--MCI), 0.907 (dementia); and for gene expression data, 0.763 (CogN), 0.761 (MCI), 0.706 (dementia). The respective F\({}_{1}\)-Score obtained for clinical data is: 0.971 (CogN), 0.939 (MCI), 0.886 (dementia), and for gene expression: 0.71 (CogN), 0.77 (MCI), 0.53 (dementia). Ref. [146] describes a study that explores natural compounds (NC) for the prevention of AD using a mix of bioinformatics and deep neural analysis. The evaluated model is Random Forest Regression (RFR) to predict the IC\({}_{50}\) (pIC\({}_{50}\)) values of ligands interacting with target proteins associated with AD. The dataset was obtained from chemical databases, but the exact size is not specified. Testing metrics include excellent recall performance. Other details (such as the dataset size) were not found. Discussions about using ML algorithms to predict brain age and assess the risk of Alzheimer's, taking into account the apolipoprotein E4 (APOE4) genotype and gender, are presented in ref. [147]. Three ML models are evaluated, namely XGBoost, RF, and LR. The dataset consists of volumetric MRI data and medical records from 1100 individuals without  
Figure 15: Comparative analysis of custom-made versus WOS results for ML algorithms in neurology (2020–2025). cognitive impairments and 602 Alzheimer's patients. The introduced metrics include brain age difference (BAD) and integrated difference (ID). The RF model was the most accurate, and the BAD ranged between 6.5 and 10 years. Ref. [148] analyzes the differences in progression from MCI to Alzheimer's in men and women using Random Survival Forests (RSF) and Shapley Additive Explanations (SHAP), evaluating the contributions of dementia biomarkers. The evaluated models are separate RSFs for men (M-RSF) and women (F-RSF). The dataset used is not specified, nor is its size. The obtained performance metrics are 0.87 for M-RSF and 0.79 for F-RSF. The results show differences in the influence of variables between sexes, with a higher risk of progression in women. Ref. [149] discusses the application of ML techniques to improve the diagnosis and prediction of AD using MRI data. It evaluates models such as the Deep Belief Network (DBN) with the Moonflower Optimization Algorithm (MOA) and compares them with others like RF, SVM, KNN, LR, and DT. The dataset from the ADNI, which comprises 1029 individuals, is used. The DBN-MOA achieved an accuracy of 97.46%, an f-Score of 93.19%, a recall of 95.79%, and a precision of 94.62%. The paper highlights the benefit of integrating demographic and medical data with MRI features. Additional advancements could include EfficientNet models and data augmentation. The challenges of ML with imbalanced datasets, particularly in the context of high-dimensional longitudinal medical data, are discussed in ref. [128]. An RF-based feature selection algorithm is evaluated and applied to imbalanced neuroimaging data for classifying AD, MCI, and normal individuals (NIs). The public ADNI dataset is used, but the exact size is not specified. The algorithm achieves higher accuracy and AUC (area under the ROC curve) values, with the best results in the NI-AD group data. Ref. [150] addresses the detection of AD and its stages using multimodal techniques that combine Positron Emission Tomography (PET) and MRI imaging. Ensemble classification models, including SVM, RF, and GB, are evaluated with feature selection based on RF. The dataset includes three classes: AD, MCI, and CogN. The size of the dataset is not specified.