Optimized Transfer Learning Based Dementia Prediction System for Rehabilitation Therapy Planning

Abstract

Abstract: Dementia is a progressive neurodegenerative disease leading to cognitive decline, and current medical interventions can only slow its progression. Early prediction of dementia onset holds potential for preventive measures. This study aims to develop a machine learning model using transfer learning from magnetic resonance imaging (MRI) data to predict dementia. The project employs k-fold cross-validation and various parameter optimization algorithms during model training to enhance prediction accuracy. The final voting classifier and optimized Transfer Learning algorithms achieved an impressive accuracy, surpassing the performance of competing methods on the same dataset. This suggests the efficacy of the proposed transferlearning approach in predicting dementia data. The developed model enables early diagnosis of dementia, a crucial factor in halting neurological deterioration associated with the disease. This is particularly relevant for regions with limited access to human physicians, providing a valuable tool for underserved populations. The proposed system holds promise for planning rehabilitation therapy programs for dementia patients. As a tool for early diagnosis and intervention, the model not only contributes to improved patient outcomes but also addresses the challenges faced by regions lacking access to sufficient healthcare resources. And also include ensemble methods (Voting Classifier, Stacking Classifier) and a hybrid CNN-LSTM approach are employed for improving accuracy, with the Voting Classifier achieving an impressive 100% accuracy. To facilitate user testing, a user-friendly front end using the Flask framework, incorporating user authentication, was proposed for seamless application of this advanced dementia prediction system.