# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 4 Results
### Review of ML on AD
The metric values obtained from tests include 99% accuracy for binary classifications AD vs. CogN and MCI vs. CogN, 91% for AD vs. MCI, and 96% accuracy for the multi-class classification AD vs. MCI vs. CogN. The size of the dataset is not available. Ref. [127] introduces RF for AD staging. It achieves 99.69% sensitivity and 99.61% F\({}_{1}\)-Score, outperforming existing methods. The study highlights challenges such as limited datasets and high computational complexity, which can be mitigated through data augmentation. Another issue is related to the feature extraction. The results surpass those of state-of-the-art methods and demonstrate the potential for clinical use. Ref. [151] evaluates the efficiency of dual-phase \({}^{18}\)F-florbetaben (FBB) imaging (dual FBB) compared to delay-phase FBB (dFBB) in diagnosing amyloid deposition in the brain using ML. Four classification models are evaluated: SVM, NB, LR, and RF. RF achieved the best results, with an F\({}_{1}\)-Score of 78.06% and AUC of 0.8724 for composite standardized uptake value ratio (SUVR) and F\({}_{1}\)-Score of 78.54% and AUC of 0.8456 for regional SUVR. Ref. [152] developed an ontology to diagnose AD and the evaluation of some ML models. An AD dataset from Kaggle and the Open Access Series of Imaging Studies (OASIS) is used. The evaluated models include LR, XGBoost, GB, Stochastic Gradient Descent (SGD), MLP, SVM, KNN, RF, and CNN. The best metric values are: MLP achieves an accuracy of 92.12%, while CNN achieves an accuracy of 94.61%. Ref. [153] addresses the evaluation of predictive genes and molecular pathways for the progression of AD and PD using human RNA sequencing transcriptomic data. The evaluated models include differential gene expression and an ML algorithm based on RFs. The analyzed dataset comes from Brodmann Area 9 in the dorsolateral prefrontal cortex,whole blood, and mononuclear cells from peripheral blood. The size of the dataset is not specified. The results suggest the existence of distinct pathogenetic mechanisms for AD and PD, with no significant overlap between the two diseases. The common ML metrics are not mentioned. Ref. [154] discusses evaluating dementia conversion in patients with MCI using non-selective metabolomic profiling. The evaluated models include an RF model. The data comes from a set of 48 patients: 19 MCI patients who progressed to AD (P-MCI) and 29 stable MCI patients (S-MCI). A panel of 20 metabolites was identified for classification, achieving an accuracy of 73.5% for classifying the three groups and 80.3% for identifying P-MCI versus S-MCI. Indole-3-propionic acid was identified as a predictor of AD progression. Ref. [155] discusses the evaluation of RF compared to other ML models. A dataset consisting of 2250 brain MRI scans was used, including 687 normal controls, 1094 MCI, and 469 AD patients, provided by the ADNI database7. The classification was performed with three feature sets (63, 29, and 22 features), and RF, MLP, and CNN achieved accuracies of 90.2%, 89.6%, and 90.5%, respectively, with 63 features. With 22 features, RF had a smaller accuracy drop (\(-\)3.8%) than MLP and CNN. The brain areas identified as important in classification were the hippocampus, amygdala, and inferior lateral ventricle. Table 4 provides a numerical summary of the previously discussed metrics to facilitate easier comparison. Table 5 concisely evaluates each approach, highlighting its strengths and weaknesses while maintaining references to the original studies. Strengths (1-5) represent scores that