This dataset contains gene expression data from individuals with varying degrees of Alzheimer's Disease (AD) and from healthy individuals. The hippocampal gene expression of nine control subjects and 22 AD subjects was analyzed using 31 separate microarrays. The correlation between each gene's expression and the Mini Mental Status Examination (MMSE) and neurofibrillary tangle (NFT) scores was then tested across all 31 subjects, regardless of their diagnosis. These tests revealed a significant transcriptional response involving thousands of genes that were strongly correlated with AD markers. Additionally, several hundred of these genes were also correlated with AD markers in only control and early-stage AD subjects (MMSE > 20). This dataset is valuable for identifying potential genes involved in AD and for diagnosing the disease using highly associated markers. However, the dataset contains 13147 genes (features) instead of just 31 samples, which presents a challenge due to the curse of dimensionality. This makes it difficult to implement machine learning and deep learning models, and dimensional reduction should be applied first.

Missing attribute values: None

Class Distribution: Sever (7/31), Moderate (8/31), Incipient (7/31), Control (9/31)

Contributors: Eric M Blalock, James Geddes, Kuey-Chu Chen, Nada Porter, William Markesbery, Philip Landfield

Database weblink = http://www.ncbi.nlm.nih.gov/geo

The DARWIN-I dataset includes handwriting images from 174 participants captured through 6 graphic tasks. It facilitates the classification of Alzheimer's disease patients versus healthy individuals, offering a unique diagnostic approach through handwriting analysis. Dive into the intricacies of motor skills and cognitive function through handwriting analysis and join the quest to unravel the mysteries of Alzheimer's disease!

CITATIONS: N. D. Cilia, T. D’Alessandro, C. De Stefano, F. Fontanella and M. Molinara, "From Online Handwriting to Synthetic Images for Alzheimer's Disease Detection Using a Deep Transfer Learning Approach," in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 12, pp. 4243-4254, Dec. 2021, doi: 10.1109/JBHI.2021.3101982.

Cilia, N.D., D’Alessandro, T., De Stefano, C. et al. Deep transfer learning algorithms applied to synthetic drawing images as a tool for supporting Alzheimer’s disease prediction. Machine Vision and Applications 33, 49 (2022). https://doi.org/10.1007/s00138-022-01297-8

N. Dalia Cilia, T. D’Alessandro, C. De Stefano and F. Fontanella, "Offline handwriting image analysis to predict Alzheimer’s disease via deep learning," 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, 2022, pp. 2807-2813, doi: 10.1109/ICPR56361.2022.9956359.

The DARWIN dataset includes handwriting data from 174 participants. The classification task consists in distinguishing Alzheimer’s disease patients from healthy people.

Creator: Francesco Fontanella

Source: https://archive.ics.uci.edu/dataset/732/darwin

The DARWIN dataset was created to allow researchers to improve the existing machine-learning methodologies for the prediction of Alzheimer's disease via handwriting analysis.

Citation Requests/Acknowledgements

N. D. Cilia, C. De Stefano, F. Fontanella, A. S. Di Freca, An experimental protocol to support cognitive impairment diagnosis by using handwriting analysis, Procedia Computer Science 141 (2018) 466–471. https://doi.org/10.1016/j.procs.2018.10.141

N. D. Cilia, G. De Gregorio, C. De Stefano, F. Fontanella, A. Marcelli, A. Parziale, Diagnosing Alzheimer’s disease from online handwriting: A novel dataset and performance benchmarking, Engineering Applications of Artificial Intelligence, Vol. 111 (20229) 104822. https://doi.org/10.1016/j.engappai.2022.104822