Alzheimer's Prediction Using Deep Neural Network and Particle Swarm Optimization

Alzheimer's disease is an irreversible neurological condition characterized by cognitive, behavioral, and memory impairments. Early prediction before the transition from mild cognitive impairment to Alzheimer's disease is still a challenging issue. This study aimed to identify factors associated with Alzheimer's disease.

This study proposes a framework for predicting Alzheimer's disease using data collected from the OASIS project, made available by the Washington University Research Center. In this study, a deep neural network was used for prediction. A particle swarm optimization (PSO) algorithm was employed for selecting appropriate features. The combination of these two methods increases the accuracy of the proposed prediction method.

The results indicate that the proposed method achieves higher accuracy with fewer features. Among the 11 features in this dataset, six features (age, socioeconomic status, Mini-mental state examination score, clinical dementia rating scale, estimated total intracranial volume, and normalized whole-brain volume) have a significant impact on predicting the disease. Among these six features, the clinical dementia rating scale is of great importance.

This study investigated the influential factors and prediction of Alzheimer's disease. Early diagnosis of Alzheimer's disease allows for the provision of appropriate diagnostic and therapeutic services, as well as an improvement in patients' quality of life. The proposed method in this study is compared with various machine learning algorithms that have shown good accuracy in predicting Alzheimer's disease. The results indicate that the accuracy of the proposed method is higher with fewer features.