# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 1 Introduction  
Dementia is a neurodegenerative condition mostly occurring in the elderly; it is characterised by difficulties in memory, orientation, and communication, psychological changes, and impairments in activities of daily living [1]. There are over 50 million people worldwide living with dementia, and this condition significantly impacts society and the economy [2]. In the United Kingdom in 2019, there were an estimated 885,000 people with dementia, and this is projected to surge by 80% by 2040 [3]. With this growing prevalence of dementia and the associated costs, research initiatives have become fundamental [4]. While pathological assessments of Alzheimer's disease (AD) diagnosis, such as using biological markers (biomarkers), and magnetic resonance imaging (MRI) [5] can be used to predict the disease, they are also time- and cost-intensive, stressful, and provide results requiring laboratory study and professional personnel who may not be available [6; 7; 8]. Therefore, cognitive assessments used for prodamental dementia such as the Alzheimer's Disease Assessment Scale-Cognitive 13 (ADAS-Cog-13), Mini Mental State Examination (MMSE), and others [9; 10] are useful since they can screen for signs of early impairment. These assessments are easier to carry out than pathological methods and show acceptable performance with reference to validity, sensitivity, and specificity [11]. However, identifying a few cognitive items that can be signs of the progression of the disease and can assistin early intervention is still a challenge [12]. Additionally, very few research studies have measured the progression of dementia using cognitive features with a data drive methodology, i.e., [6; 8; 13; 14; 15]. While [PERSON] et al. [8] used machine learning to assess cognitive measures, their study focused on diagnosing AD and not the progression of the disease, which is more challenging, and it did not consider the criteria defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) framework [16]. Moreover, [PERSON] et al. [6] investigated functional elements that may have an influence on dementia advancement using real data from the ADNI data project, and no cognitive elements were considered in the evaluation. In addition, [PERSON] et al. [15] studied real data related to the Clinical Dementia Rating-Sum of Boxes (CDR-SB) assessment method in the ADNI project to reveal dementia indicators and understand their impact on dementia diagnosis. The authors have not considered symptoms of dementia progression. Mapping between the assessed cognitive items and DSM-5's criteria would have been helpful for the clinicians' understanding of linking the machine learning results and the actual diagnosis of the disease. To fill the gap, the aims of this research are:  
1. To improve current neuropsychological method performances in terms of dementia detection rate using machine learning;
2. To identify the few cognitive items that can be symptoms of the advancement of dementia experimentally;
3. To use a classification algorithm with the ability to provide clinicians with a useful and easy-to-understand knowledge derived from real data related to the dementia stages. The scope of our research is limited to neuropsychological assessments--results related to other pathological procedures, neuroimaging, or biomarkers are excluded. To achieve this aim, we present an enhanced version algorithm called the Alzheimer's Disease Class Rules (AD-CR), which extends a rule-based classification method [17] to model cognitive activities from real data subjects related to the ADNI data repository [18]. In this research, we attempt to offer the following benefits to clinicians:  
1. The ability to predict the signs of dementia progression objectively using sets of features and machine learning to analyse the data subjects;
2. To disseminate a rule-based classification model comprised of rules on the association of the cognitive items to the DSM-5 framework as a toolkit that can be used during prodromal dementia assessment;
3.