# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 2 Literature Review  
In this section, we initially introduce the cognitive assessment used in this research and then provide relevant recent literature on cognitive research related to rule-based classification for predicting dementia based solely on cognitive assessments or a combination of cognitive assessments with other pathological assessments. Neuroimaging or neuropathological dementia prediction systems are out of the scope of this article. Moreover, systems that use non-rule-based classification algorithms like deep learning or deep ANN are not part of this research scope. ADAS-Cog is a medical assessment designed to measure the level of a patient's cognitive dysfunction [9]. While its use for monitoring pre-dementia and Mild Cognitive Impairment (MCI) has been criticised [19], it is generally accepted as one of the commonly used procedures for assessing dementia. ADAS-Cog is typically administered by a professional and consists of several tasks: word recall, naming objects and fingers, commands, constructional praxis, ideational praxis, orientation, word recognition, language, comprehension of spoken language, word finding, and remembering test instructions. ADAS-Cog usually takes an hour to complete in a clinical setting and produces a score between 0 and 70, with 70 indicating the most severe cognitive dysfunction. In addition to the 11-question version, some variations exist. The ADAS-Cog 13 additionally contains a delayed word recall section, a maze or number cancellation section, and is scored between 0 and 85 [20]. [PERSON] et al. [21] tested the ADAS-Cog's ability to diagnose dementia on a sample of 451 subjects (of which there were 254 control subjects with normal cognition, 86 with MCI and 111 with AD). They found that the best cut-off score for describing AD was \(\geq\)12, which had a sensitivity of 89.19% and a specificity of 88.53%. For our research, we are interested in the findings of the ADAS-Cog-13 ([PERSON] et al., 1997)--the dataset retrieved from the ADNI uses this version. [PERSON] et al. [22] investigated the problem of dementia detection when using the Everyday Cognition (ECog) test based on real data subjects collected from ADNI (TADPOLE) [23]. In particular, data subjects (diagnosed as CN, MCI, and AD) who participated in ECog's two versions, the patient and study partner, were analysed using machine learning algorithms with a focus on rule-based classifiers. The results obtained using the machine learning algorithms showed that when the input dataset was balanced prior to the training phase, the classification performance in terms of accuracy was enhanced for the models derived. Moreover, rule-based classifiers such as RIPPER, PART and C4.5-Rules, besides random forest, derived competitive classifiers for dementia detection. [PERSON] et al. [24] introduced a rule-based machine learning approach called the Sparse High-Order Interaction Model with Rejection Option (SHIMR) to predict dementia. The authors utilised data subjects from the ADNI dataset with some proteomics features as well as cerebrospinal fluid-related features collected from 141 data subjects. A comparative analysis of the data subjects was conducted using the C4.5 algorithm [25], and the rule-based approach (SHIMR). Results derived based on classification accuracy revealed that the SHIMR approach was superior to C4.5 by deriving classifiers with 84% accuracy on the data subjects. [PERSON] et al. [26] applied machine learning algorithms to clinical data to help physicians diagnose dementia more accurately. The authors applied their model to the CREDOS study consisting of data gathered by 37 universities in Korea from 2005-2013. The model recognises the need for a diagnostic process that is made up of four steps or modules. First, the module: the kScale variable selection method is used because it provides flexibility by verifying various results from several methods. Next, a classification model is built to determine dementia symptoms.