Predicting Early Stages Of Alzheimer's Disease And Identifying Key Biomarkers Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
arXiv:2607.02142v1 Announce Type: new Abstract: Alzheimers disease (AD) is a brain disorder that develops slowly and mainly affects memory, thinking, language, and daily activities. It is one of the most common causes of dementia and creates many difficulties for patients as well as their families. In the early stage, the symptoms are often mild and may look like normal ageing. For this reason, many people are diagnosed late, when the disease has already progressed. At present, there is no complete cure for AD. Still, early detection can help doctors manage the condition better and take suitab
Advances in deep learning and machine learning methodologies have reached a maturity that allows for sophisticated pattern recognition in complex medical data, making early disease prediction more feasible than ever before.
Early and accurate detection of Alzheimer's disease can significantly improve patient outcomes through timely intervention and management, reducing the long-term societal burden of the disease.
This research introduces a refined capability for early detection of Alzheimer's, shifting from symptomatic diagnosis to predictive insights based on key biomarkers, potentially altering treatment paradigms.
- · Healthcare providers
- · Pharmaceutical companies developing AD treatments
- · Patients with Alzheimer's and their families
- · Diagnostic imaging companies
- · Late-stage AD care facilities (potentially, due to fewer patients)
- · Traditional diagnostic methods
Improved early diagnosis rates for Alzheimer's disease.
Increased investment and research into early-stage AD treatments and preventative measures.
Potential for a societal shift in how neurodegenerative diseases are managed, moving towards proactive and personalized medicine.
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Read at arXiv cs.LG