A new machine learning method for identifying Alzheimer's disease

Liu, Lin; Zhao, Shenghui; Chen, Hai-Bao; Wang, Aiguo

doi:10.1016/j.simpat.2019.102023

Cited by 117 publications

(61 citation statements)

References 14 publications

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“…ML models are highly acknowledged in real-time clinical practice and also in diagnosis and AD treatment selection [ 41 ]. Several MRI works have been integrated into ML models to make AD predictions [ 12 , 17 , 42 ], but there has been no comprehensive model to amplify model accuracy. In view of this, we introduced a hybrid model to enhance the precise detection of AD based on the analysis of MRIs.…”

Section: Discussionmentioning

confidence: 99%

“…It has been hypothesized that ML-supervised methods generate the knowledge of features necessary to correlate AD sample data [ 16 ]. It is also reported that logistic regression, coupled with cross-validation, can enhance the accuracy of AD prediction by speech amalgamation [ 17 ]. On the other hand, support vectors, along with feature reduction techniques, were able to classify dementia subjects with 70% accuracy [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

Battineni

Chintalapudi

Amenta

et al. 2020

JCM

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Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

Battineni

Chintalapudi

Amenta

et al. 2020

JCM

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show abstract

“…Genellikle hafif bilişsel bozulma evresi diğer demans nedenlerinin başlangıcı ile karıştırılabildiğinden hastalığın tanısı birçok neden elendikten sonra konulabilmektedir [1]. Bu problemden dolayı hastalığın erken evre teşhisi için tahmin, sınıflandırma ve yapay zekâ çalışmaları literatürde halen çalışılan konulardır [5,6]. AH'ye benzer şekilde medikal görüntüler kullanılarak diğer hastalıkların analizlerinde de yapay zekâ çalışmaları son zamanlarda popüler bir şekilde artmaktadır [7].…”

Section: Gi̇ri̇ş (Introduction)unclassified

3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi

Özi̇ç

Ekmekçi

Özşen³

et al. 2022

Politeknik Dergisi

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“…Previous research has shown that speech can be used to distinguish between healthy and AD patients (Pulido et al, 2020 ). Some researchers have focused on developing new machine learning model architectures to improve detection (Chen et al, 2019 ; Chien et al, 2019 ; Liu et al, 2020 ), while others have used language models (Guo et al, 2019 ) to classify AD. Others have focused on trying to extract acoustic and text features that capture information indicative of AD.…”

Section: Introductionmentioning

confidence: 99%

Classifying Alzheimer's Disease Using Audio and Text-Based Representations of Speech

Haulcy

Glass

2021

Front. Psychol.

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Alzheimer's Disease (AD) is a form of dementia that affects the memory, cognition, and motor skills of patients. Extensive research has been done to develop accessible, cost-effective, and non-invasive techniques for the automatic detection of AD. Previous research has shown that speech can be used to distinguish between healthy patients and afflicted patients. In this paper, the ADReSS dataset, a dataset balanced by gender and age, was used to automatically classify AD from spontaneous speech. The performance of five classifiers, as well as a convolutional neural network and long short-term memory network, was compared when trained on audio features (i-vectors and x-vectors) and text features (word vectors, BERT embeddings, LIWC features, and CLAN features). The same audio and text features were used to train five regression models to predict the Mini-Mental State Examination score for each patient, a score that has a maximum value of 30. The top-performing classification models were the support vector machine and random forest classifiers trained on BERT embeddings, which both achieved an accuracy of 85.4% on the test set. The best-performing regression model was the gradient boosting regression model trained on BERT embeddings and CLAN features, which had a root mean squared error of 4.56 on the test set. The performance on both tasks illustrates the feasibility of using speech to classify AD and predict neuropsychological scores.

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A new machine learning method for identifying Alzheimer's disease

Cited by 117 publications

References 14 publications

A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi

Classifying Alzheimer's Disease Using Audio and Text-Based Representations of Speech

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