# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 3 The Algorithm
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Classification Algorithms** & **Features** & **Dataset** & **Medical Assessment Method** & **Aim** \\ \hline \multirow{4}{*}{[27]} & Decision Trees (C4.5), Naive Bayes, and Logistic Regression & Cognitive items, Functional activities measured by the Instrumental Activities of Daily Living (IADL), and items related to Certain Coles (GDS) method & Two datasets: Clinical diagnosis and CDR & To identify a few clinical indicators to classify individuals as dependent, MCI or Cognitively Normal Institutional Review Board & Clinical Dementia Rating (CDR) and clinical diagnosis techniques \\ \hline \multirow{4}{*}{[26]} & ANN, Decision Tree, and support vector machine (SVM) & Cognitive, functional, and demographic items & CREDOS dataset; data collected by 37 universities in Korea from 2005-2013 & Clinical Dementia Rating-Sum of Boxes (CDR-SB) & To identify clinical measures that can help classify data subjects with dementia levels, if any \\ \hline \multirow{4}{*}{[28]} & SVM, Random Forest, Naive Bayes, and Logistic Regression & Pathological, cognitive, behavioural, and demographic items & Primary care data from NHS Neuron (now part of Northern, Eastern and Western Devon Clinical Commissioning Group) & Multiple pathological and behavioural assessments & To implement a system that is able to detect dementia during clinical evaluation \\ \hline \multirow{4}{*}{[24]} & SVM, Random Forest, Decision Tree & Biomarkers, cognitive tests’ total scores, and demographic items & Biomarkers Consortium Plasma Proteomics Project RBM multiplex data, and ADNI-Merge common cognitive tests’ scores & MMSE score, cerebrospinal fluid (CSF) and plasma protein features like tau, amyloid-\(\beta\) (AB) and phosphorylated tau (p-tau) proteins & To design an affordable dementia diagnosis system using a data-driven process \\ \hline \multirow{4}{*}{[22]} & RIPPER, PART, Random Forest and Decision Tree (C4.5) & eCOG cognitive items & ADNI & Everyday Cognition (eCOG) test (patient and informant versions) & Compare rule-based classification on dementia prediction \\ \hline \multirow{4}{*}{[6]} & Decision Trees (C4.5), Bayesian Network, and Logistic Regression & ADAS-13 Cognitive items, demographics attributes & ADAS-13 sheet + ADNI & ADAS-13 cognitive test & Assessing cognitive items in the progression using machine learning techniques \\ \hline \multirow{4}{*}{[29]} & Decision Trees (C4.5), Bayesian Network, and Logistic Regression & EAQ Functional items, demographics attributes & FAQ sheet + ADNI & FAQ test & Assessing functional elements for dementia progression using a data-driven process with rule-based algorithms and non-rule-based algorithms \\ \hline \multirow{4}{*}{[31]} & Decision Tree, Random Forest and the local interpretable explanation (LIME) & Demographic information, medical history, physical explanatory rating studies & Demographic characteristics, YES/NO questions related to health and well-being, rating scales, medical history, physical examinations, neuropsychological assessments, and performance of cognitive tests & \multirow{4}{*}{Assisting clinicians in the diagnosis of dementia by accessible machine learning models} \\ \cline{1-1} \cline{2-2}  & DS-ANFIS, C4.5, SVM, Random Forest, SEGKD, SLAVEZ, QuickRules & Demographic, clinical variables & Open Access Series of Imaging Studies (OASIS) repository & Demographic, Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), and Global Deterioration Scale (GDS) & Developing a fuzzy logic-based automated system for the diagnosis of dementia \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the recent literature on rule-based classification models for dementia prediction and progression.