# The Application of Intelligent Data Models for Dementia Classification
## 4 Discussion  
[PERSON] and [PERSON] (2014) [33] employed ADNI data, neuroimaging software, and machine learning algorithms to conduct an empirical study on multivariate pattern analysis (MVPA). Prior MVPA studies had focused on extracting imaging measurements or on improving prediction accuracy through statistical analysis algorithms ([PERSON], [PERSON] & [PERSON], 2009 [34]; [PERSON], [PERSON], [PERSON] & [PERSON], 2012 [35]; [PERSON] et al., 2007) [36]. [PERSON] and [PERSON] (2014) [33] used MRI and neuropsychological features with three MVPA algorithms: Support Vector Machine (SVM), Relevance Voxel Machine (RVoxM), and Neighborhood Approximate Forest (NAF) ([PERSON] & [PERSON], 1995 [37]; [PERSON], [PERSON], [PERSON], 2013 [38]; [PERSON] & [PERSON], 2012 [39]). The results indicated that a stronger predictive accuracy can be observed when using an integration of MRI variables, rather than on one MRI result, which is typically univariate and gives information on one anatomical area of the brain than when analyzing them individually. This corresponds with other earlier research works that indicate some neurocognitive disorders are related to larger scale networks across multiple brain regions ([PERSON] et al., 2009) [40]. The authors conclude that there is no one universal method for dealing with neuroimaging data but rather five main limiting factors that influence prediction accuracy: sample size, data quality, standardized imaging measurements, standardized  
Figure 3: ROC area results. prediction algorithms, and the underlying biological footprint of the disease. In addition, the study also showed that other variables, such as demographic and neuropsychological, can be predicted using MRI data. Four supervised learning methods were tested by [PERSON] et al. (2013) [PERSON] et al. (2013) to classify AD patients and controls, as well as to predict AD conversion from MCI. The authors used data from the AddNeuroMed cohort, which though a different initiative to ADNI, the data acquisition process was designed to be compatible with ADNI ([PERSON] et al., 2009, 2010) [42; 43]. Orthogonal partial least squares to latent structures (OPLS), Decision Trees, artificial neural networks (ANN), and support vector machines (SVM) have been applied on MRI features, MRI + age, MRI + education, and MRI + APOE to generate models. The authors also used the feature selection algorithm method named Relief ([PERSON] & [PERSON], 1992) [12] to select the top ten features (nine areas of the brain area and cerebrospinal fluid (CSF) volumes). The models derived by all considered classification algorithms showed high specificity and sensitivity when tasked with distinguishing between AD and CN, with results between 82-85%. Similarly, 80-85% of MCI-converters were able to be identified. However, performance was much poorer when predicting MCI-non-converters, where roughly half or more (49-68.4%) were diagnosed as cognitively normal. The results indicated no significant statistical difference in the performance when all the MRI variables were used, versus the ranked features only. There was a noticeable decline in performance when age or education was included in the Decision Tree algorithm. Other than that, there was no significant influence for better or worse on the performance when age, education, or APOE were included with the MRI data. The highest accuracy and AUC scores were observed when MRI + APOE was used with ANN, and MRI + Edu with SVM and OPLS, with scores of 91-92%. Models with approximately 86% accuracy were derived when OPLS was used on an ADNI study using the same features ([PERSON] et al., 2011) [44]. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], (2019) [45] utilized deep learning to combine MRI, demographic, neuropsychological, and APOE data focusing on patients who were likely to progress from MCI to AD within three years. The authors developed a feature extractor sub-network, which performs dual tasks--AD to CN classification, as well as a MCI to AD conversion prediction.