Analysis of Artificial Neural Networks on Predicting Dementia

Abstract

Dementia, a chronic neurological disorder characterized by memory loss and impaired cognitive ability, has affected tens of millions of people globally and projections estimate this number to rise in the coming decade. Despite its widespread impact, dementia remains one of many diseases with no true cure or definitive method of diagnosis. This study aims to explore the question of how artificial neural networks can be used to predict dementia. Analyzing current research on neural networks for disease diagnosis highlights a notable gap, known as the "black box conundrum," indicating the challenge of tracing steps leading to a given output. Another gap in the literature is the absence of comparative data on factors influencing successful neural network algorithms. Conducting a comprehensive cross-continental comparative analysis across North America, Asia, and Europe, and utilizing an experimental design with the UCI ML Repository’s Parkinson’s Database, this study specifically aims to fill the identified gaps. The findings reveal key elements for successful neural networks: a data sample exceeding 100, clinically intelligible data values, and a backpropagation model. In the experimental phase, a comparison between a backpropagation model and logistic regression, paired with Shapley value analysis, elucidated a 94.87% accuracy for the former, surpassing the latter's 92.3%. These findings extend beyond dementia, providing valuable insights for neural network applications in all healthcare settings. Future research endeavors should focus on refining and optimizing neural networks with the ultimate goal of achieving 100% accuracy and maximized efficiency.