# The Application of Intelligent Data Models for Dementia Classification
## 3 Results  
### Alzheimer's Disease Neuroimaging Initiative (ADNI)  
ADNI is a collection of medical centers and universities in the USA and Canada that was established to develop standardized biomarker procedures and neuroimaging techniques to better assess and diagnose subjects with AD, those with MCI, and those who are cognitively normal (CN) ([PERSON] et al., 2009) [16]. Commencing in 2004, under the leadership of Dr. [PERSON] as a six-year study (ADNI-1) (ADNI, 2017a) [17], the primary goal was to develop and validate biomarkers to use as outcome measures in clinical trials. The purpose of these biomarkers is to aid in the (early) diagnosis and tracking of AD (ADNI, 2017a [17]; [PERSON] et al., 2009 [16]). The biomarkers include scores from cognitive and functional tests, demographic data, MRI, readings from PET procedures, and cerebrospinal fluid markers.  
Subsequent studies (ADNI GO, ADNI-2, ADNI-3) were later initiated, where the scope was broadened to include further participants of different clinical stages (early and late mild cognitive impairment groups were introduced) (ADNI, 2017a) [17; 18]. Other studies focused on an understanding of the longitudinal sequence of dementia, and investigated technologies that provide additional measures (such as the use of mass spectroscopy for the analysis of CSF markers and MRI scans to detect tau-protein tangles, included in ADNI  
Figure 1: Data process followed.  
datasets such as [PERSON], 2011 [19], [PERSON] et al., 2017b [20], [PERSON], [PERSON], 2011 [21].  
The data used in this study was originally derived from the ADNI database (ADNI, 2017a) [17], and it is called Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) dataset, which was made available in 2017 [18]. The dataset consists of 1737 unique patients with their demographic information, and the neuropsychological and medical data associated with each patient visit. Each assessment is recorded separately, supplying 12,741 dataset instances.  
The dataset consists of three main subject groups (class labels): CN, MCI, and Dementia. These three groups consist of 523, 341, and 866 subjects, respectively, after pre-processing. We observed an imbalance in the class distribution with the MCI group being over twice the size of the Dementia group. The average age of a participant in each group is similar, between 73-75 years, though both the MCI (73.03 \(\pm\) 7.60 years) and Dementia (74.93 \(\pm\) 7.81 years) groups see a greater spread of ages in comparison to the CN group (74.25 \(\pm\) 5.79 years).  
Participants in the Dementia group were likely to have a lower mean education than the other groups, with the CN group having the highest education on average. Participants in the CN group were also noticeably more likely to have not be currently married at the time of the study. In total, 67.75% of CN subjects we analyzed were married, compared to 83.63% and 76.97% in the Dementia and MCI groups, respectively. CN subjects were much less likely to be carriers of the APOE 4 allele (28.60%) compared to the Dementia (65.77%) and MCI (50.23%) groups. A total of 17.27% of CN participants had a change in their diagnosis by the end of this study's data and nearly half of the MCI group (42.01%) exhibited a change. It is important to note that for 12.88% of the MCI subjects that changed diagnosis, it was a positive change with the new diagnosis being CN.