Neurodegenerative disorders such as Parkinson’s disease, Alzheimer’s disease, and Amyotrophic Lateral Sclerosis (ALS) are progressive conditions that result in the deterioration of neuronal function. Early diagnosis remains a significant challenge due to the subtle onset of symptoms and the lack of accessible diagnostic tools, particularly in low-resource settings. These challenges often lead to delayed interventions and poor patient outcomes. This research proposes an innovative healthcare solution for the early prediction and monitoring of neurodegenerative diseases, utilizing artificial intelligence (AI) and wearable technology. The proposed system integrates an AI-powered mobile application that analyzes patient medical history using secure Aadhaar-based authentication. The model utilizes Recurrent Neural Networks (RNNs) and transfer learning to detect early-stage neurodegeneration. Although the system is currently in a conceptual stage, preliminary testing was performed using simulated patient data to verify workflow functionality. The complete model is designed to be evaluated using benchmark datasets, such as ADNI and MIMIC-III, with metrics including accuracy, F1-score, and ROC-AUC. The wearable device continuously monitors vital signs and provides real-time alerts for patients and guardians. This comprehensive framework addresses gaps in early diagnosis, enhances accessibility, and supports proactive care for underserved communities.
Harrisha et al. (Wed,) studied this question.