# Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
## 1 Introduction  
Quality of life has increased with the development of medical technology, and as the average human lifespan increases the senior population is growing. Additionally, the pace of aging is accelerating. Countries today are facing an aging society, which poses many changes and challenges for society [1,2]. In particular, the number of dementia patients is increasing because of the increase of the senior population. Dementia is the most prevalent degenerative disease in seniors. There are 47.5 million people living with dementia around the world, a majority of whom (58%) live in middle- and low-income countries. Each year brings 7.7 million new cases of dementia [1]. The number of dementia patients is expected to more than triple by 2050. As the number of dementia patients increases dramatically, the socioeconomic, psychological, physical, and economic burdens for dependents' families are also increasing [2]. Dementia can be sorted into dementia caused by Alzheimer's disease, cerebrovascular dementia, hypothyroidism, benign brain tumors, etc. Alzheimer's accounts for about 60% to 70% of dementia patients; it is caused by aging, family history, and depression. However, the presence of [PERSON]'s disease can serve to delay the progression of dementia when it leads to early diagnosis. Cerebrovascular dementia, which affects about 20% to 30% of commented patients, is caused by diseases such ashypertension, heart disease, diabetes, arteriosclerosis, cerebral hemorrhage, and cerebral infarction. Cerebrovascular dementia can be prevented through risk factor management, and can be treated by medicine. Other dementia can be treated by surgery, such as removing the thyroid or benign brain tumor. Previous research on dementia was focused on treatment and care after the onset of the disease. However, as mentioned before, early diagnosis may delay the progression of dementia [3]. Generally, there are three stages to dementia diagnosis. The first stage is a screening test for cognitive ability using the MMSE-KC (Mini-Mental State Examination in the Korean version of the CERAD Assessment Packet). The second stage involves performing neuropsychological assessment using CERAD-K (the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease) for those who are not diagnosed as normal in the screening test. The final stage is to diagnose (R/O) for dementia or mild cognitive impairment (MCI) by doctor consultation and carer interview. After the third stage, the suspected patients are definitively diagnosed using MRI or CT and blood tests in hospital. As a result, patients are classified into categories of normal, MCI, and dementia. In this paper, we propose a two-layer model for the early diagnosis of dementia, inspired by the diagnosis approach used in dementia support centers and using machine learning methods. The first layer is a screening test to classify subjects as normal or abnormal, while the second layer is close examination, classifying cases as MCI or dementia. In the first stage, data preprocessing is performed based on the MMSE-KC data. The next step is to select the required features. Once the feature selection is completed, the data are learned by the selected features, and classified into normal and cognitive decline groups. Finally, the first step classifies the normal group. In the second stage, CERAD-K data are learned, using machine learning algorithms, for classifying MCI and dementia. Therefore, the structure of the model is similar to the existing dementia screening method, and its effect is simplifying the dementia screening process. The data were collected from patients who visit and are tested at the Gangbuk-Gu center for dementia in Seoul, Republic of Korea. We collected patient information such as age, gender, education, and test results using MMSE-KC and CERAD-K. To these data we applied machine learning techniques, which are useful for data analysis and are used in various domains.