# The Application of Intelligent Data Models for Dementia Classification
## 4 Discussion
Furthermore, since the feature representations are multi-layered, rich and complex data, such as MRI, can be handled well in the classification process ([PERSON] et al., 2019) [45]. The authors used four input combinations of biomarkers: clinical data + MRI, clinical + Jacobian Determinant data, clinical + atlas masked MRI, and clinical + Jacobian Determinant + MRI. The best classification performance is seen where MRI + clinical data are used in conjunction, with a median AUC of 92.50% and an accuracy of 86%. A decrease is observed when brain areas that are not classically associated with AD are removed, down to 92.20%. Further decreases in performance are observed when all variables are used, and when the MRI data only is used (no clinical data), a much lower AUC and accuracy, of 70% and 72%, respectively, is seen. [PERSON] et al. (2010) [46] studied the MCI--AD conversion, trying to predict a continuous measure, such as scores from the MMSE, Dementia Rating Scale (DRS), ADAS-Cog and RAVLT, from MRI data. Relevance Vector Regression (RVR) was applied for prediction using MRI data and measures from four cognitive tests, namely MMSE, DRS, ADAS-Cog, and RAVLT. The results of their research indicated a strong linear relationship between DRS, MMSE, and ADAS-Cog scores with grey matter (GM) segments of MRI data, but did not indicate this with the RAVLT assessment. DRS, followed by MMSE in dataset 1, and ADAS-Cog, followed by MMSE in datasets 2 and 3, provided the best predictive power when processed. The author suggested that images of the whole brain provided a better correlation with these cognitive tests, as they test multiple domains within the brain, whereas RAVLT targets memory, which is associated with the medial temporal lobe. The predictive accuracy of the models worsened when large groups of a class, for example CN or MCI, were either missing or removed. Further, the years-of-education feature was also shown to be significantly correlated to MMSE and ADAS-Cog from the ADNI dataset. The authors demonstrated that RVR can be a useful multivariate method for investigating the relationship between MRI features and clinical scores. Their results support the use of MMSE, ADAS-Cog, and DRS in dementia applications. [PERSON] et al. (2017) [47] experimented on cognitive test scores using MRI, PET, and previous cognitive information from an ADNI cohort. The authors showed that being able to accurately predict cognitive test scores would be useful in the diagnosis of AD as well as for assisting clinicians to manage patients. The aim of this study was to predict the scores of MMSE, CDRS, RAVLT, ADAS11, and ADAS13 tests. To do this, stochastic gradient boosting of Decision Trees was applied to a sample of over 1141 patients from the ADNI data. Specifically, the scores at the 24-month mark are predicted based on information at prior visits (6 months, 12 months, 18 months). The results of the experimentations indicated high correlation (\(\geq\)0.9) across all correlation measures. The authors compared the results from the gradient-boosted method against other algorithms such as Multilayer Perceptron, 10-KNN, Decision Trees, Bagging, and SVM. Gradient boosting outperformed other methods of predicting the cognitive test scores. [PERSON] et al. (2020) [48] used 741 ADNI-participants with blood microarray data to measure the cognitive decline related to AD in terms of CDR score. The authors used the CDR scores recorded in the last clinical assessment to categorize the data into three groups, and then applied machine learning algorithms to forecast the cognitive level of the individual using the reported blood microarray data. The results showed that one chloride intracellular channel 1 (CLIC1) probe was significant; the predictive rate achieved using the machine learning algorithm reached 87% when considering nonsignificant probes. [PERSON] et al.