Early detection of dementia using artificial intelligence and multimodal features with a focus on neuroimaging: A systematic literature review

Health and Technology, Journal Year: 2024, Volume and Issue: 14(2), P. 201 - 237

Published: Feb. 10, 2024

Language: Английский

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A Comparative Study of Pretrained Deep Neural Networks for Classifying Alzheimer's and Parkinson's Disease

2021 IEEE Symposium Series on Computational Intelligence (SSCI), Journal Year: 2023, Volume and Issue: unknown, P. 1334 - 1339

Published: Dec. 5, 2023

Early detection of neurodegenerative diseases can be challenging, where Deep Learning (DL) techniques have shown promise. Most DL provide a robust and accurate classification performance. However, due to the complex architectures models, results are difficult interpret, causing challenges for their adoption in healthcare industry. To facilitate this, current work proposes an effective interpretable analysis pipeline that compares performances pre-trained models early Alzheimer's Disease (AD) Parkinson's (PD). The proposed allows tuning hyperparameters, such as batch size, number epochs, learning rates, achieve more classification. Additionally, validation predictions using heatmaps drawn from GradCAM also provided.

Language: Английский

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Assessing the Reliability of Machine Learning Models Applied to the Mental Health Domain Using Explainable AI

Published: March 4, 2024

Machine Learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, recall may indicate performance of models but not necessarily reliability their outcomes. This paper assesses effectiveness a number machine learning algorithms applied to an important dataset domain, specifically, mental health, by employing explainability methodologies. Using multiple model techniques, project provides insights into workings help determine algorithm predictions. The results are intuitive. It was found that were focusing significantly on less relevant features at times, unsound ranking make therefore argues it for research provide addition other accuracy. particularly applications critical domains such as healthcare. A future direction investigate methods quantify terms from explainability.

Language: Английский

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Assessing the Interpretability of Machine Learning Models in Early Detection of Alzheimer's Disease

Published: July 8, 2024

Alzheimer's disease (AD) is a chronic and irreversible neurological disorder, making early detection essential for managing its progression.This study investigates the coherence of SHAP values with medical scientific truth.It examines three types features: clinical, demographic, FreeSurfer extracted from MRI scans.A set six ML classifiers are investigated their interpretability levels.This validated on OASIS-3 dataset binary classification.The results show that clinical data outperforms others, margin 14% over features, second-best features.In case explanations provided by tree-based consistently align insights.This comparison was calculated using Kendall Tau distance.

Language: Английский

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Understanding Feature Importance of Prediction Models Based on Lung Cancer Primary Care Data

2022 International Joint Conference on Neural Networks (IJCNN), Journal Year: 2024, Volume and Issue: 12, P. 1 - 8

Published: June 30, 2024

Language: Английский

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