# The Application of Intelligent Data Models for Dementia Classification
## 5 Conclusions  
Neuropsychological assessment tools, such as the CDR, are commonplace in the diagnosis and assessment of neurocognitive disorders, including dementia. Compared to the clinical procedures (such as MRI and PET) that are often used in conjunction with these assessments, the neuropsychological assessments are relatively much less invasive, for instance, requiring a questionnaire or observational-based assessment. Being able to accurately predict dementia status in a patient based only on these types of tests (andknown demographic data, which can likely also be easily obtained) could see great clinical use and application to aid in the early diagnosis of dementia. To investigate the issue of having a potentially accurate, quick, and non-invasive method for dementia classification, our research and experimentation focused on investigating the ability of different classification algorithms to perform the task as outlined above. This was done by applying different machine learning algorithms including NB, C4.5, RIPPER, SVM, and KNN, to the CDR-SB clinical scores and demographic data from an ADNI dataset. Our results indicate competitive performance when classifying subjects into CN, MCI, or Dementia, using baseline demographic data and CDR-SB scores. Though we observed fairly strong results across all algorithms we utilized, there was still variation in the performance ability, indicating some algorithms are better suited to the task of classifying dementia status based on our baseline data. NB, C4.5, and RIPPER exhibited sensitivities and specificities between 88.4-89.7% and 91.7-93.2%, respectively, being able to correctly assign CN, MCI, or AD to the majority of subjects. Though shown to be a strong contender as a classifier in many related literary works, SVM in our experimentation did not perform as well as expected. We did, however, utilize a linear SVM algorithm, which is a likely cause of this notable difference. Similarly, KNN gave results of between 10-20% less than the higher performing algorithms. A limitation of this study was that the algorithms were only applied to one subset of ADNI data, which exhibited some class imbalance. This may have negatively affected the KNN and other algorithms. Furthermore, the ADNI dataset we have utilized (TADPOLE challenge) does not represent a wide spread of demographic variables, with many of the participants being Caucasian individuals with, on average, a higher level of education. Another possible limitation is that the CDR-SB assessment was used by clinicians with other neuropsychological tests, and the clinicians' experience to assign the class label for many of the participants of the ADNI study during each medical visit re-assessment. Thereby, the decision to whether an individual is Idemented or not could be affected by some of the CDR-SB's elements. This study shows that CDR may be adequate in assessing and assigning a dementia class to patients on their early visit to speed up clinical procedures. Other medical procedures (such as MRI, PET scans) would likely confirm the diagnostic result obtained by neuropsychological tests like CDR. However, the relative expertise required to conduct a CDR assessment and the resources required to do so could see this as a reliable method for diagnosis by clinicians. Further analysis and comparisons would need to be conducted to validate and corroborate these results. In the future, we intend to broaden the scope of this research by utilizing other neurocognitive assessment data alone and in conjunction with CDR, to see whether it can be incorporated to confer classification improvements. We did not use medical data that is readily available in ADNI in our experimentation, such as MRI and PET. The inclusion of this alters the practicality of a non-invasive, quick, and efficient method to class dementia patients in a clinical setting; however, may confer increased sensitivity and specificity in classifying dementia. Conceptualization, F.T. ; Methodology, F.T., R.A, Y.W. ; Software, Y.W.