Optimizing Machine Learning Models for Accessible Early Cognitive Impairment Prediction: A Novel Cost-effective Model Selection Algorithm.

Cognitive impairment and dementia-related diseases develop several years before moderate or severe deterioration in cognitive function occurs. Nevertheless, most dementia cases, especially in low- and middle-income countries, remain undiagnosed because of limited access to affordable diagnostic tools. Additionally, the development of accessible tools for diagnosing and predicting cognitive impairment has not been extensively discussed in the literature. The objective of this study is to develop a cost-effective and highly accessible machine learning model to predict the risk of cognitive impairment for up to five years before clinical insight. We utilized easily accessible data from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) to train and evaluate various machine learning and deep learning models. A novel algorithm was developed to facilitate the selection of cost-effective models that offer high performance while minimizing development and operational costs. We conducted various assessments, including feature selection, time-series analyses, and external validation of the selected model. Our findings indicated that the Support Vector Machine (SVM) model was preferred over other high-performing neural network models because of its computational efficiency, achieving F2-scores of 0.828 in cross-validation and 0.750 in a generalizability test. Additionally, we found that demographic and historical health data are valuable for early prediction of cognitive impairment. This study demonstrates the potential of developing accessible solutions to predict cognitive impairment early using accurate and efficient machine learning models. Future interventions should consider creating cost-effective assessment tools to support global action plans and reduce the risk of cognitive impairment.