# Alzheimers Disease Diagnosis using Machine Learning: A Review

**Authors:** Nair Bini Balakrishnan, P. S. Sreeja, Jisha Jose Panackal  
**arXiv:** [2304.09178](https://arxiv.org/abs/2304.09178)  
**Date:** 2023-04-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2304.09178v1/

## Key points

- Alzheimer's Disease (AD) is a severe neurological condition causing brain cell degeneration and progressive memory loss.
- It primarily affects the elderly population, leading to cognitive and biological function decline and brain shrinkage (atrophy).
- Accurate diagnosis of AD requires cutting-edge methods like machine learning, which has gained attention in the medical industry.
- Individuals with AD struggle more with basic tasks as the disease progresses, potentially leading to complete cessation of brain function in severe cases.
- Machine learning methods can help determine early-stage Alzheimer's disease likelihood in individuals.
- A recent study focused on AD diagnosis using deep learning techniques and reinforcement learning found that deep learning methods excel at feature extraction and accurate AD classification.
- Deep Reinforcement Learning (DRL) methods are underutilized in image processing for dementia detection compared to deep learning techniques.
- There is potential for exploring DRL further in dementia detection based on comparisons between deep learning and reinforcement learning.
- Leveraging advanced machine learning techniques like DRL is crucial for precise Alzheimer's disease diagnosis, emphasizing the need for continued research into innovative approaches for enhancing diagnostic capabilities in neurodegenerative diseases.

## Summary

Alzheimer's Disease (AD) is a severe neurological condition that causes the degeneration of brain cells and progressive memory loss. It primarily affects the elderly population and leads to a decline in cognitive and biological functions, ultimately resulting in brain shrinkage known as atrophy. Accurate diagnosis of AD requires cutting-edge methods such as machine learning, which has gained significant attention in the medical industry. As the disease progresses, individuals with AD struggle more with basic tasks and may experience complete cessation of brain function in severe cases. Machine learning methods can help determine the likelihood of early-stage Alzheimer's disease in individuals. A recent study focused on papers from 2008 to 2023 found on Google Scholar related to AD diagnosis using deep learning techniques and reinforcement learning. The analysis included sixty relevant papers that were examined based on AD biomarkers and machine-learning techniques utilized. The findings revealed that deep learning methods exhibit a remarkable ability to extract features and accurately classify AD. However, Deep Reinforcement Learning (DRL) methods have been underutilized in image processing for dementia detection. The comparison between deep learning and reinforcement learning highlighted the potential for exploring DRL further in dementia detection. This review underscores the importance of leveraging advanced machine learning techniques for precise Alzheimer's disease diagnosis and emphasizes the need for continued research into innovative approaches like DRL for enhancing diagnostic capabilities in neurodegenerative diseases.