# Gamified AI Approch for Early Detection of Dementia

arXiv: https://arxiv.org/abs/2405.16538 · HTML: https://mlsanity.com/paper/2405.16538/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 79 references that are in the map.
- First posted on arXiv 26 May 2024.

## Builds on (most cited first, 3 of 3)

- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [Flattened Convolutional Neural Networks for Feedforward Acceleration](https://mlsanity.com/paper/1412.5474/) (2014)
- [1D CNN Architectures for Music Genre Classification](https://mlsanity.com/paper/2105.07302/) (2021)

## Abstract (arXiv)

This paper aims to develop a new deep learning-inspired gaming approach for early detection of dementia. This research integrates a robust convolutional neural network (CNN)-based model for early dementia detection using health metrics data as well as facial image data through a cognitive assessment-based gaming application. We have collected 1000 data samples of health metrics dataset from Apollo Diagnostic Center Kolkata that is labeled as either demented or non-demented for the training of MOD-1D-CNN for the game level 1 and another dataset of facial images containing 1800 facial data that are labeled as either demented or non-demented is collected by our research team for the training of MOD-2D-CNN model in-game level 2. In our work, the loss for the proposed MOD-1D-CNN model is 0.2692 and the highest accuracy is 70.50% for identifying the dementia traits using real-life health metrics data. Similarly, the proposed MOD-2D-CNN model loss is 0.1755 and the highest accuracy is obtained here 95.72% for recognizing the dementia status using real-life face-based image data. Therefore, a rule-based weightage method is applied to combine both the proposed methods to achieve the final decision. The MOD-1D-CNN and MOD-2D-CNN models are more lightweight and computationally efficient alternatives because they have a significantly lower number of parameters when compared to the other state-of-the-art models. We have compared their accuracies and parameters with the other state-of-the-art deep learning models.