# The Application of Intelligent Data Models for Dementia Classification  
[PERSON]  
1 Computer Science Department, Prince Hussein Bin Abdullah College for Information Technology,  
Al Al Al-Bayt University, Mafra 25113, Jordan  
As such, having an efficient yet accurate way of diagnosing AD status is essential to increase the chance of early diagnosis and, therefore, treatment and planning.  
AD is typically diagnosed by a combination of neuropsychological and medical assessments, such as the Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) ([PERSON], [PERSON], 1984) [5], the Mini Mental State Exam (MMSE) ([PERSON], [PERSON], 1975) [6], and Clinical Dementia Rating (CDR) ([PERSON], [PERSON], [PERSON], [PERSON], 1982) [7]. Information is also derived from Magnetic Resonance Imagery (MRI) ([PERSON], [PERSON] & [PERSON], 1999) [8], Positron Emission Tomography (PET) ([PERSON], [PERSON], [PERSON] & [PERSON], 2017) [9], genetic variables (APOE4) ([PERSON] et al., 2017) [10], and more. As diagnosis is usually multifaceted, it may require several assessments by skilled clinicians involving much time and cost. If a neuropsychological assessment, as mentioned above, was able to give a clear dementia classification accuracy on its own, this could widen the accessibility of dementia assessments to those in poorer socio-economic areas while reducing the cost and need for assessment by skilled professionals.  
In this research, we have chosen to focus on the CDR-SB as there is no extensive research on the use of intelligent techniques focusing on CDR-SB scores besides it can be considered affordable assessment. In an initial experimentation, CDR-SB showed a correlation with dementia status in the outcomes of four feature selection techniques applied to the ADNI dataset including Correlation Feature Set (CFS) ([PERSON], 1999) [11], Relieff ([PERSON] & [PERSON], 1992) [12], and two Wrappers ([PERSON] & [PERSON], 1999) [13]. However, this could be attributed to that CDR was used as a measure to assign the diagnostic label by clinicians during the ADNI project besides other tests.  
The problem we consider in this research is a classification task, whereby we investigated and measured the performance of classification algorithms on CDR-SB scores and other demographic data to predict dementia. Our research differs from much of the existing literature, which focuses on intelligent techniques applied to MRI and other clinical data. We will focus on the use of machine learning techniques on neuropsychological data to create models to predict the clinical diagnosis for patients. Particularly, we test models derived by various classification techniques using real data and compare the predictive performance of the derived models to determine the ideal model for dementia detection. The main questions we seek to answer are:  
Would a model derived from using machine learning on the ADNI dataset that was based on the CDR-SB scores and demographic variables be feasible to predict dementia?  
If so, which classification technique could produce highly accurate models based on accuracy, sensitivity, and specificity rates?  
The potential application of machine learning, if successful, could see an increase in detection accuracy and efficiency for clinicians. The models produced can be used to aid the decision-making process during screening or diagnosis of dementia-related conditions.  
The paper sets out the methodology used in our research, followed by a literature review of research that has focused on the use of machine learning technology in predicting the dementia status of patients. The review focuses predominantly on research that has used data from the ADNI data repository. Following this review, we describe the dataset and experimental procedure used to generate our results, along with an in-depth discussion and our conclusion.