# Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
## 2 Related Works  
Dementia, an illness of the brain, attacks cognitive activities such as memory, rationality, and thought. It is caused either by old age or traumatic injury, with approximately 60-70% of cases attributable to [PERSON]'s disease [4]. Dementia increases in severity the longer it goes undiagnosed. The process of diagnosis involves three steps: the first involves consulting a physician; the second consists of completing an array of neuropsychological tests; the third involves an MRI scan [5]. This paper addresses the early diagnosis of dementia by means of neuropsychological testing in tandem with demographic information. Commonly used neuropsychological measures include the Mini-Mental State Examination (MMSE), the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), the Blessed Orientation-Memory-Concentration Test (BOMC), the Montreal Cognitive Assessment (MoCA), a brief informant interview to detect dementia (AD8), and the General Practitioner Assessment of Cognition (GPCOG), with each presenting certain advantages and limitations. MMSE and CERAD are currently most used, since they can be administered regardless of the subject's gender, education, culture or religion [5; 6; 7; 8]. MMSE-KC (Mini-Mental State Examination in the Korean version of the CERAD Assessment Packet) is used to screen and measure impairment of cognitive function. The MMSE tests and scores six domains (1) orientation, (2) registration, (3) attention and calculation, (4) recall, (5) language and (6) constructional ability [9; 10]. Tables 1 and 2 show the contents of the neuropsychological assessment tests used in the screening test (MMSE-KC) and the precise examination (CERAD-K) used in this paper. CERAD-K (the Korean version of the CERAD neuropsychological assessment battery) is mainly used for scrutiny. CERAD began with researchers at sixteen major Alzheimer's research centers in the United States and was developed for standard diagnosis and evaluation of Alzheimer's patients [11; 12]. \begin{table}
\begin{tabular}{c c} \hline
**Category** & **Description** \\ \hline Orientation to time & From broadest to most narrow. Orientation to time has been correlated with future decline \\ \hline Orientation to place & From broadest to most narrow. This is sometimes narrowed down to streets, and sometimes to floor \\ \hline Registration & Repeating named prompts \\ \hline Recall & Registration recall \\ \hline Attention and calculation & Serial sevens, or spelling “world” backwards. It has been suggested that serial sevens may be more appropriate in a population where English is not the first language \\ \hline Language & Naming a pencil and a watch \\ \hline Repetition & Speaking back a phrase \\ \hline Complex commands & Draw pentagonal overlap. \\ \hline Three-stage commands & Give the patient a piece of plain blank paper and ask him to follow your instructions \\ \hline Understanding and decision & Understanding and decision \\ \hline \end{tabular}
\end{table} 
Table 1: Mini-Mental State Examination in the Korean version of the CERAD (Consortium to Establ  
CERAD can examine areas that are more intense than MMSE, such as language fluency, verbal memory ability, time span configuration ability, and depression. Because analysis and decision-making about the results from these sorts of tests depend on the inclinations of the psychologist (and thus human error cannot be avoided), machine-based analysis and data mining approaches have been widely used to alleviate inconsistencies. In this paper, machine learning algorithms are explored to determine if the analysis of neuropsychological and demographic data can be automated for the early diagnosis of dementia. According to [PERSON] and [PERSON] [13] in their study of various statistical and machine learning methods, a Bayesian-network classifier and a SVM performed best in assessing participants afflicted by little or no dementia. In a study conducted by [PERSON] et al.