Machine Learning Framework for Early Alzheimer’s Identification Using Cognitive Performance Measures

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder that affects memory, cognitive abilities, and
daily functioning. Early identification of Alzheimer’s is crucial for timely intervention and improved patient
care. This project presents a machine learning framework for the early detection of Alzheimer’s disease using
cognitive performance measures. The system collects patient data through structured cognitive assessments,
including memory recall, language ability, attention span, and problem-solving skills.The collected data is
preprocessed and analyzed to ensure consistency and accuracy. An ensemble machine learning model is
employed to improve prediction performance by combining multiple algorithms, thereby increasing reliability
and reducing error rates. The model evaluates the input cognitive scores and predicts whether a patient is
likely to have Alzheimer’s disease, along with a probability score indicating the confidence of the
prediction.The framework is implemented using the Django web framework, providing a user-friendly
interface for clinicians to input patient data, perform assessments, and view results. The system also maintains
a database of patient records and prediction outcomes for future reference and analysis. Additionally, an
integrated chatbot assists users by providing basic guidance related to Alzheimer’s symptoms.The proposed
system aims to support healthcare professionals by offering a fast, accurate, and efficient tool for early
Alzheimer’s detection. By leveraging machine learning techniques and cognitive performance data, this
framework contributes to improved diagnostic processes and enhances decision-making in clinical
environments