Devising an interpretable calibrated scale to quantitatively assess the dementia stage of subjects with alzheimer's disease: A machine learning approach

Aditya, C.R.; Pande, Mandaar

doi:10.1016/j.imu.2016.12.004

Cited by 11 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reportedly, people with dementia can be accurately categorized using supervised models in conjunction with feature selection [16]. In another investigation [17], multifactor affiliation analysis was used to categorize patients according to the interrelationships of features. This method excels beyond classification trees and generic distribution zones in classifying patients and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification

et al. 2023

View full text Add to dashboard Cite

Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer’s disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to diagnose AD, with an accuracy of only 85 per cent. This did not allow for a correct diagnosis, which could be validated only through postmortem examination. Diagnosis of AD can be sped up, and the course of the disease can be predicted by applying machine learning (ML) techniques to Magnetic Resonance Imaging (MRI) techniques. Dementia in specific seniors could be predicted using data from AD screenings and ML classifiers. Classifier performance for AD subjects can be enhanced by including demographic information from the MRI and the patient’s preexisting conditions. In this article, we have used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In addition, we proposed a framework for the AD/non-AD classification of dementia patients using longitudinal brain MRI features and Deep Belief Network (DBN) trained with the Mayfly Optimization Algorithm (MOA). An IoT-enabled portable MR imaging device is used to capture real-time patient MR images and identify anomalies in MRI scans to detect and classify AD. Our experiments validate that the predictive power of all models is greatly enhanced by including early information about comorbidities and medication characteristics. The random forest model outclasses other models in terms of precision. This research is the first to examine how AD forecasting can benefit from using multimodal time-series data. The ability to distinguish between healthy and diseased patients is demonstrated by the DBN-MOA accuracy of 97.456%, f-Score of 93.187 %, recall of 95.789 % and precision of 94.621% achieved by the proposed technique. The experimental results of this research demonstrate the efficacy, superiority, and applicability of the DBN-MOA algorithm developed for the purpose of AD diagnosis.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification

et al. 2023

View full text Add to dashboard Cite

show abstract

“…It is reported that the successful classification of dementia subjects can be done by supervised models associated with feature selection [16]. In another study, patient classification was accomplished via multifactor affiliation analysis with the inter feature relationships [17]. This technique helps in getting better patient classification and produce higher performance compared with classification trees and generic-distribution zones [17].…”

Section: Introductionmentioning

confidence: 99%

“…In another study, patient classification was accomplished via multifactor affiliation analysis with the inter feature relationships [17]. This technique helps in getting better patient classification and produce higher performance compared with classification trees and generic-distribution zones [17]. The above approaches did not highlight the importance of data-centric ML techniques and the adoption of model boosting knowledge, which can transform weak learners into strong learners and improve model performance.…”

Section: Introductionmentioning

confidence: 99%

Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms

et al. 2021

View full text Add to dashboard Cite

Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.

show abstract

“…ML models, coupled with MRI information, can provide high diagnostic accuracy of age-related cognitive decline (ARCD) in dementia subjects [ 15 ]. 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 ].…”

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

View full text Add to dashboard Cite

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.

show abstract

Devising an interpretable calibrated scale to quantitatively assess the dementia stage of subjects with alzheimer's disease: A machine learning approach

Cited by 11 publications

References 36 publications

Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification

Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification

Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms

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

Contact Info

Product

Resources

About