Study the combination of brain MRI imaging and other datatypes to improve Alzheimer's disease diagnosis
Stubblefield, J.; Kronberger, A.; Causey, J.; Qualls, J.; Fowler, J.; Zeng, K.; Walker, K.; Huang, X.
Show abstract
Alzheimers Disease (AD) is a degenerative brain disease and is the most common cause of dementia. Despite being a common disease, AD is poorly understood. Current medical treatments for AD are aimed at slowing the progression of the disease. So early detection of AD is important to intervene at an early stage of the disease. In recent years, by using machine learning predictive algorithms, assisted clinic diagnosis has received great attention due to its success of machine learning advances in the domains of computer vision. In this study, we have combined brain MRI imaging features and the features of other datatypes, and adopted various models, including XGBoost, logistic regression, and k-Nearest Neighbors, to improve AD diagnosis. We evaluated the models on the benchmark dataset of Alzheimers Disease Neuroimaging Initiative. Experiment results show that the logistic regression model is the top performer in terms of evaluation metrics of precision, recall, and F1-score. The prediction of the models could provide valuable information for diagnosis and prognosis of patients with suspected Alzheimers disease. The XGBoost model achieves a comparable performance and has the potential to serve as a valuable diagnostic tool for patients with suspected AD with its self-validation by rediscovering previously known associations with AD.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Network-based identification of genetic factors in Ageing, lifestyle and Type 2 Diabetes that Influence in the progression of Alzheimer’s disease 91%
- An Inexpensive Smartphone-Based Device and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes 90%
- Generalizable electroencephalographic classification of Parkinson’s Disease using deep learning 89%
Similar papers in this journal
- Random forest model for feature-based Alzheimer's disease conversion prediction from early mild cognitive impairment subjects 98%
- c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer’s disease 96%
- Interpretable multivariate survival models: Improving predictions for conversion from mild cognitive impairment to Alzheimers disease (AD) via data fusion and machine learning 96%
Similar papers in this journal
- Quantitative longitudinal predictions of Alzheimer's disease by multi-modal predictive learning 95%
- Common molecular signatures between coronavirus infection and Alzheimer's disease reveal targets for drug development 93%
- Profiles of cognitive change in preclinical Alzheimer's disease using change-point analysis 92%
Similar papers in this journal
- Phenotyping Neuropsychiatric Symptoms Profiles of Alzheimer's Disease Using Cluster Analysis on EEG Power 92%
- Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias 92%
- Detection of cognitive decline using a single-channel EEG with an interactive assessment tool 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.