Explainable Machine Learning Model for Alzheimer Detection Using ...

Recent research has revealed that using machine learning systems for the analysis of genetic data could reliably detect Alzheimer's disease.

An explainable machine learning approach for Alzheimer's disease ...

A two-layer RF model approach was proposed in study for diagnosis and progression detection of AD. The first layer acts as a multi ...

Explainable machine learning has the potential to increase the accuracy and interpretability of Alzheimer's disease detection models, giving ...

An explainable machine learning based prediction model ... - Frontiers

In particular, we employ ensemble learning and feature selection methods to develop an explainable prediction model for AD and MCI. Five feature selection ...

An Explainable AI Paradigm for Alzheimer's Diagnosis Using Deep ...

The proposed framework aims to enhance the interpretability of deep learning models by incorporating XAI techniques, allowing clinicians to ...

(PDF) Explainable Machine Learning Model for Alzheimer Detection ...

Recent research has revealed that using machine learning systems for the analysis of genetic data could reliably detect Alzheimer's disease.

In-depth insights into Alzheimer's disease by using explainable ...

Supervised Machine Learning (ML) has already shown to be effective in discrimination between AD patients from cognitively normal subjects by ...

An explainable machine learning model of cognitive decline derived ...

Multimodal artificial intelligence technologies using only speech data promise improved detection of neurodegenerative disorders. Methods: ...

Explainable Artificial Intelligence in Alzheimer's Disease Classification

The emerging field of explainable AI (XAI) aims to justify the trustworthiness of these models' predictions. This work presents a systematic ...

Explainable AI for Alzheimer Detection: A Review of Current ... - MDPI

The neuron-level explainability provided by LAVA ensures that model predictions can be linked to specific neural activity in a deep learning network, ...

An explainable machine learning model of cognitive decline derived ...

Traditional Alzheimer's disease (AD) and mild cognitive impairment (MCI) screening lacks the sensitivity and timeliness required to detect ...

Explainable AI-based Alzheimer's prediction and management ...

Machine learning is a potential technique for Alzheimer's diagnosis but general users do not trust machine learning models due to the black-box nature. Even, ...

A cross-sectional study of explainable machine learning ... - Frontiers

In explainable ML it is estimated how much each feature contributes to the model's classification. The importance of each feature was evaluated ...

Interpreting artificial intelligence models: a systematic review on the ...

ML and DL models have also been used extensively in AD prediction due to their ability to analyse large amounts of data and identify patterns ...

Interpretable machine learning for dementia: A systematic review

Machine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact.

Explainable Deep-Learning-Based Diagnosis of Alzheimer's ...

The experimental results show that the proposed model achieved a classification accuracy of 73.90% on the ADNI database. Then, we provide an ...

ANALYZE-AD: A comparative analysis of novel AI approaches for ...

Antor et al. [30] performed a comparative analysis of machine learning algorithms to predict Alzheimer's disease focused on dementia detection using the Open ...

(PDF) An explainable machine learning approach for Alzheimer's ...

Machine learning (ML) models offer a promising tool for identifying individuals at risk of AD. However, current research tends to prioritize ML accuracy while ...

Explainable Machine Learning with Pairwise Interactions for ... - MDPI

To improve predicting MCI-to-AD conversion, it is necessary to consider various factors using explainable machine learning (XAI) models which ...

"An Explainable Deep Learning Prediction Model for Severity of ...

This thesis addresses the challenge of building an explainable deep learning model for a clinical application: predicting the severity of Alzheimer's disease ( ...