Robust prediction of cognitive test scores in Alzheimer's patients

Izquierdo, Walter; Martin, Harold L.; Cabrerizo, Mercedes; Barreto, Armando; Andrian, Jean; Rishe, Naphtali; Gonzalez‐Arias, Sergio; Loewenstein, David; Duara, Ranjan; Adjouadi, Malek

doi:10.1109/spmb.2017.8257059

Cited by 8 publications

(12 citation statements)

References 6 publications

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“…The extracted features including hazard score are used with support vector machine (SVM) classifier as it is proved to be one of the powerful classifiers and performance of the enhanced GBDT classifier has been compared with SVM. 30,2,27,25 The analysis has been carried out by varying the number of features to perform the classification. The impact of features on the classification has been analysed and tabulated in Table 3.…”

Section: Resultsmentioning

confidence: 99%

“…The learners are adjusted based on a differential function computed based on the misclassified inputs. The accuracy achieved is high compared to other classification methods 24,25…”

Section: Related Workmentioning

confidence: 88%

“…The defect of decision trees is their scope as they can work only on small data. 21 The efficiency of decision trees can be improved by more complex trees which use plenty of subtrees as in the case of Random Forests 9,[22][23][24][25] have proposed the usage of Gradient Boosted Decision Trees(GBDT) as an effective algorithm in classification due to the high efficiency that is associated with them. The algorithm weighs the misclassified inputs in each stage and focuses on accommodating them in the next stage.…”

Section: Related Workmentioning

confidence: 99%

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Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Jayashree

Srinivasan

et al. 2022

Health Informatics J

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Alzheimer’s disease (AD) is one of the most common forms of dementia contributing to more than 70% of the cases. The factors accounting for the cause and progression of neurodegenerative diseases like AD are primarily genetic, in addition to life style and environmental factors. Early and accurate diagnoses of AD empower practitioners to take timely clinical decisions and preventive actions. This being the motivation, the work proposes a novel pattern matching and scoring method on genetic material towards devising an effective classifier. We propose a distinctive disease causing gene sequence pattern identification using suffix trees as a base detection model with an accuracy of 91.5% in linear time complexity. A scoring mechanism is implemented to assign scores to genes based on the severity of the disease causing and disease resistant Single Nucleotide Polymorphisms associated with the genes. These scores are then used as a remarkable feature in the gradient boosted decision tree classifier to enhance the classification of AD versus healthy control. The efficiency of the proposed gene powered EGBDT classifier is evaluated on ADNI benchmark data set with the prediction accuracy of 94.16% and is found to be efficient compared to the recent works in the literature.

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Section: Resultsmentioning

confidence: 99%

“…The learners are adjusted based on a differential function computed based on the misclassified inputs. The accuracy achieved is high compared to other classification methods 24,25…”

Section: Related Workmentioning

confidence: 88%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Jayashree

Srinivasan

et al. 2022

Health Informatics J

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“…Neuroimaging pattern classification methods have demonstrated recent advances in predicting Alzheimer's disease (AD) and mild cognitive impairment (MCI) from magnetic resonance imaging (MRI) and positron emission tomography (PET) scans [1,2,3]. Since the brain is an extremely complex system, large improvements in understanding the brain's organization have been made by representing the brain as a connectivity graph [4].…”

Section: Introductionmentioning

confidence: 99%

Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis

Zhou,

He,

Zhang

et al. 2022

Preprint

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Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting the difference of features between regions in healthy control (HC), mild cognitive impairment (MCI), and AD groups. We conducted the experiments on the ADNI database with imaging data from three modalities, including VBM-MRI, FDG-PET, and AV45-PET, and showed that the ROI features learned by our method were effective for enhancing the performances of both clinical score prediction and disease status identification. It also successfully identified biomarkers associated with AD and MCI.

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“…For example, previous works employed an elastic net linear regression model [38,39] to predict changes in cognitive test scores as a representative for cognitive ability over time. The same task has also been approached using ML estimators such as gradient boosting (GB) [40,41] and recurrent neural networks (RNN) [42,43,44]. The most common targets when predicting cognitive decline are the Mini Mental Status Test (MMSE) [45] and the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) [46] scores [47,48,49].…”

Section: Introductionmentioning

confidence: 99%

Predicting Progression & Cognitive Decline in Amyloid-Positive Patients with Alzheimer's Disease

Dansson

Stempfle

Egilsdóttir

et al. 2021

Preprint

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BackgroundIn Alzheimer’s disease (AD), amyloid- β (Aβ) peptides aggregate in the brain forming amyloid plaques, which are a key pathological hallmark of the disease. However, plaques may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with Aβ pathology. MethodsA cohort of n= 2293 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database was selected to study heterogeneity in disease progression for individuals with Aβ plaque pathology. The analysis used baseline clinical variables including demographics, genetic markers and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the limited prevalence of Aβ pathology, models fit only to Aβ-positive subjects were compared to models fit to an extended cohort including subjects without established Aβ pathology, adjusting for covariate differences between the cohorts. ResultsAβ pathology status was determined based the Aβ 42 /Aβ 40 ratio. The best predictive model of change in cognitive test scores for Aβ-positive subjects at the two-year follow-up achieved an R 2 score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F1 score of 0.791. Conforming to expectations, Aβ-positive subjects declined faster on average than those without Aβ pathology, but the specific level of Aβ plaques was not predictive of progression rate. For the four-year prediction task of cognitive score change, the best model achieved an R 2 score of 0.325 and it was found that fitting models to the extended cohort substantially improved performance. Moreover, using all clinical variables outperformed the best model based only on baseline cognitive test scores which achieved an R 2 score of 0.228. ConclusionOur analysis shows that levels of Aβ plaques are not strong predictors of the rate of cognitive decline in Aβ-positive subjects. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of two-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.

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Robust prediction of cognitive test scores in Alzheimer's patients

Cited by 8 publications

References 6 publications

Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis

Predicting Progression & Cognitive Decline in Amyloid-Positive Patients with Alzheimer's Disease

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Robust prediction of cognitive test scores in Alzheimer's patients

Cited by 8 publications

References 6 publications

Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Clinical decision support system for early detection of Alzheimer’s disease using an enhanced gradient boosted decision tree classifier

Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis

Predicting Progression &amp; Cognitive Decline in Amyloid-Positive Patients with Alzheimer's Disease

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Predicting Progression & Cognitive Decline in Amyloid-Positive Patients with Alzheimer's Disease