Abstract
Dementia rates are projected to increase significantly by 2050, posing considerable challenges for healthcare systems worldwide. Developing efficient diagnostic tools is critical, and machine learning (ML) algorithms have shown potential for improving the accuracy of cognitive impairment classification. This study aims to address challenges in current systems by leveraging readily available electronic medical record (EMR) data to simplify and enhance the classification of cognitive impairment. The analysis includes 283 older adults, categorized into three groups: 144 individuals with mild cognitive impairment (MCI), 38 with dementia, and 101 healthy controls. Various ML techniques are evaluated to classify cognitive performance levels based on input features such as sociodemographic variables, lab results, comorbidities, Body Mass Index (BMI), and functional scales. Key predictors for distinguishing healthy controls from individuals with MCI are identified. These are history of myocardial infarction, vitamin D3 levels, the Instrumental Activities of Daily Living (IADL) scale, age, and sodium levels. The nonlinear Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel achieve the best performance for MCI classification, with an accuracy of 69%, an AUC of 0.75, and a Matthews Correlation Coefficient (MCC) of 0.43. For distinguishing healthy controls from those with dementia, the most influential factors include the IADL scale, the Activities of Daily Living (ADL) scale, education, vitamin D3 levels, and age. Here, the Random Forest algorithm demonstrates superior performance, achieving 84% accuracy, an AUC of 0.96, and an MCC of 0.71. These two models consistently outperform other ML techniques, such as K-Nearest Neighbors, Multi-Layer Perceptron, linear SVM, Naive Bayes, Quadratic Discriminant Analysis, Linear Discriminant Analysis, AdaBoost, and Gaussian Process Classifiers. The findings suggest that EMR data can be an effective resource for the initial classification of cognitive impairments. Integrating these ML-driven approaches into primary care settings may facilitate the early identification of older patients who could benefit from further cognitive assessments.