Predictive Modeling for Early Alzheimer’s Disease Using Natural Language Processing

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions worldwide, leading to cognitive decline and placing a substantial burden on families and healthcare systems. Early detection is crucial for managing the disease and improving patient outcomes, yet current diagnostic methods are often invasive, expensive, or insufficiently sensitive in the early stages. Utilizing data from the DementiaBank Pitt Corpus, we developed a machine learning model capable of differentiating between individuals with AD and healthy controls based on their text. The Multilayer Perceptron (MLP) model achieved an accuracy of 86.93%, with a precision of 0.87 for detecting AD cases and 0.84 for identifying non-AD cases. The recall rates were 0.83 for AD and 0.88 for non-AD, indicating the model’s robustness in correctly identifying both conditions. These results highlight the potential of NLP-based models as effective diagnostic tools, offering a non-invasive and cost-effective alternative to traditional methods. The implementation of such technology could revolutionize the early detection of AD, enabling timely interventions that may slow disease progression and improve the quality of life for patients. By integrating this model into clinical settings or remote health applications, we can enhance accessibility to diagnostic services, particularly in under served communities.