Predicting progression of Alzheimer’s disease using forward-to-backward bi-directional network with integrative imputation

Ho, Ngoc-Huynh; Yang, Hyung-Jeong; Kim, Jahae; Dao, Duy-Phuong; Park, Hyuk-Ro; Pant, Sudarshan

doi:10.1016/j.neunet.2022.03.016

Cited by 17 publications

(9 citation statements)

References 39 publications

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“…After tuning the model's parameters, the accuracy improved to ∼80%. Previously, the best accuracy to classify AD as progressive or nonprogressive was 58.47% [ 23 ]. The achieved accuracy with AdaBoost with effective preprocessing has improved by 21.34% compared to the literature, reaching 80%.…”

Section: Resultsmentioning

confidence: 99%

“…To demonstrate the effectiveness of the proposed model with the baseline work presented in Ho et al [ 23 ], Table 5 provides the accuracy, precision, and recall of both models for AD progression and nonprogression classification, respectively. There is a significant increase in the observed accuracy with AdaBoost.…”

Section: Resultsmentioning

confidence: 99%

“…The model encompassed several new methods to analyze the disease progression, utilizing cognitive and neuroimaging data. Compared to the state‐of‐the‐art literature [ 23 ], our proposed method has almost 20% better classification accuracy.…”

Section: Introductionmentioning

confidence: 84%

“…To this end, as far as our knowledge is concerned, a single study has worked on the detection of patients with a progressive nature [ 23 ], which has utilized a univariate recurrent neural network (RNN). In contrast, our study proposes a multivariate long short‐term memory (M‐LSTM) for the detection of AD subjects with a progressive nature.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning in neurological disorders: A multivariate LSTM and AdaBoost approach to Alzheimer's disease time series analysis

Irfan,

Shahrestani,

Elkhodr

2024

Health Care Science

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IntroductionAlzheimer's disease (AD) is a progressive brain disorder that impairs cognitive functions, behavior, and memory. Early detection is crucial as it can slow down the progression of AD. However, early diagnosis and monitoring of AD's advancement pose significant challenges due to the necessity for complex cognitive assessments and medical tests.MethodsThis study introduces a data acquisition technique and a preprocessing pipeline, combined with multivariate long short‐term memory (M‐LSTM) and AdaBoost models. These models utilize biomarkers from cognitive assessments and neuroimaging scans to detect the progression of AD in patients, using The AD Prediction of Longitudinal Evolution challenge cohort from the Alzheimer's Disease Neuroimaging Initiative database.ResultsThe methodology proposed in this study significantly improved performance metrics. The testing accuracy reached 80% with the AdaBoost model, while the M‐LSTM model achieved an accuracy of 82%. This represents a 20% increase in accuracy compared to a recent similar study.DiscussionThe findings indicate that the multivariate model, specifically the M‐LSTM, is more effective in identifying the progression of AD compared to the AdaBoost model and methodologies used in recent research.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning in neurological disorders: A multivariate LSTM and AdaBoost approach to Alzheimer's disease time series analysis

Irfan,

Shahrestani,

Elkhodr

2024

Health Care Science

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show abstract

“…Numerous neurologists and medical researchers are currently devoting significant work to developing procedures for early diagnosis of AD, with consistently encouraging findings 12 . In recent decades, several studies have been proposed for automatic detection of AD 6 , 13 – 16 . Various neuroimaging signals such as magnetic resonance imaging (MRI) 17 , 18 , functional magnetic resonance imaging (fMRI) 19 , 20 , positron emission tomography (PET) 21 , 22 , electroencephalography (EEG) 23 – 25 , and magnetoencephalography (MEG) 26 , 27 have been investigated to determine if there are any anomalous clustering coefficients or distinctive path lengths in the brain networks of AD patients.…”

Section: Introductionmentioning

confidence: 99%

Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay

Jeong

Kim

2023

Sci Rep

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Early identification and treatment of moderate cognitive impairment (MCI) can halt or postpone Alzheimer’s disease (AD) and preserve brain function. For prompt diagnosis and AD reversal, precise prediction in the early and late phases of MCI is essential. This research investigates multimodal framework-based multitask learning in the following situations: (1) Differentiating early mild cognitive impairment (eMCI) from late MCI and (2) predicting when an MCI patient would acquire AD. Clinical data and two radiomics features on three brain areas deduced from magnetic resonance imaging were investigated (MRI). We proposed an attention-based module, Stack Polynomial Attention Network (SPAN), to firmly encode clinical and radiomics data input characteristics for successful representation from a small dataset. To improve multimodal data learning, we computed a potent factor using adaptive exponential decay (AED). We used experiments from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort study, which included 249 eMCI and 427 lMCI participants at baseline visits. The proposed multimodal strategy yielded the best c-index score in time prediction of MCI to AD conversion (0.85) and the best accuracy in MCI-stage categorization ($$83.19\%$$ 83.19 % ). Moreover, our performance was equivalent to that of contemporary research.

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Discrete-Time Replicator Equations on Parallel Neural Networks

Bagdasaryan,

Kalampakas,

Saburov

2024

Communications in Computer and Information Science

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Predicting progression of Alzheimer’s disease using forward-to-backward bi-directional network with integrative imputation

Cited by 17 publications

References 39 publications

Machine learning in neurological disorders: A multivariate LSTM and AdaBoost approach to Alzheimer's disease time series analysis

Machine learning in neurological disorders: A multivariate LSTM and AdaBoost approach to Alzheimer's disease time series analysis

Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay

Discrete-Time Replicator Equations on Parallel Neural Networks

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