Machine Learning Framework for Implementing Alzheimer’s Disease

Dhanalakshmi, J.; Ansari, Gufran Ahmad

doi:10.1109/iccsp48568.2020.9182220

Cited by 30 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, as a machine learning technique for AD-assisted diagnosis, the transfer network described in this research offers several advantages in terms of computational efficiency and training time savings. Additionally, this work employs solely MRI data to classify AD, whereas the literature [ 18 ] relies on multimodal classification approaches to achieve high classification accuracy. Along with MRI, data from PET and cerebrospinal fluid are analyzed.…”

Section: Methodsmentioning

confidence: 99%

Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders

Gupta

Saravanan²,

Choudhury³

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Alzheimer’s disease is incurable at the moment. If it can be appropriately diagnosed, the correct treatment can postpone the patient’s illness. To aid in the diagnosis of Alzheimer’s disease and to minimize the time and expense associated with manual diagnosis, a machine learning technique is employed, and a transfer learning method based on 3D MRI data is proposed. Machine learning algorithms can dramatically reduce the time and effort required for human treatment of Alzheimer’s disease. This approach extracts bottleneck features from the M-Net migration network and then adds a top layer to supervised training to further decrease the dimensionality and delete portions. As a consequence, the transfer network presented in this study has several advantages in terms of computational efficiency and training time savings when used as a machine learning approach for AD-assisted diagnosis. Finally, the properties of all subject slices are combined and trained in the classification layer, completing the categorization of Alzheimer’s disease symptoms and standard control. The results show that this strategy has a 1.5 percentage point better classification accuracy than the one that relies exclusively on VGG16 to extract bottleneck features. This strategy could cut the time it takes for the network to learn and improve its ability to classify things. The experiment shows that the method works by using data from OASIS. A typical transfer learning network’s classification accuracy is about 8% better with this method than with a typical network, and it takes about 1/60 of the time with this method.

show abstract

Section: Methodsmentioning

confidence: 99%

Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders

Gupta

Saravanan²,

Choudhury³

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…To find the different stages in AD, the BLS diagnosing model and convolutional variants were used by Gao et al (2020) and Sivakani and Ansari (2020). The validity of the model was evaluated using MRI data images from the ADNI database.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Performance Metrics for Machine Learning Classifiers with a Focus on Alzheimer's Disease Data

Rajayyan¹,

Mustafa²

2023

AIP

View full text Add to dashboard Cite

Alzheimer's disease is a brain memory loss disease. Usually, it will affect persons over 60 years of age. The literature has revealed that it is quite difficult to diagnose the disease, so researchers are trying to predict the disease in the early stage. This paper proposes a framework to classify Alzheimer's patients and to predict the best classification algorithm. The Bestfirst and CfssubsetEval methods are used for feature selection. A multi-class classification is done using machine learning algorithms, namely the naïve Bayes algorithm, the logistic algorithm, the SMO/SMV algorithm and the random forest algorithm. The classification accuracy of the algorithms is 67.68%, 84.58%, 87.42%, and 88.90% respectively. The validation applied is 10-fold cross-validation. Then, a confusion matrix is generated and class-wise performance is analysed to find the best algorithm. The ADNI database is used for the implementation process. To compare the performance of the proposed model, the OASIS dataset is applied to the model with the same algorithms and the accuracy of the algorithms is 98%, 99%, 99% and 100% respectively. Also, the time for the model construction is compared for both datasets. The proposed work is compared with existing studies to check the efficiency of the proposed model.

show abstract

“…During the training process, an SA provides feedback about the prediction accuracy. SA is widely used in data classification process for different applications such as early detection and prediction of diabetes [ 24 , 25 , 26 , 27 ], prediction of Alzheimer’s Disease [ 28 , 29 , 30 , 31 ], detection of Acute Respiratory Distress Syndrome [ 32 , 33 , 34 ] and EEG Signal Processing [ 35 , 36 , 37 , 38 ].…”

Section: Supervised Machine-learning Approachesmentioning

confidence: 99%

EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

et al. 2022

View full text Add to dashboard Cite

There has recently been increasing interest in postural stability aimed at gaining a better understanding of the human postural system. This system controls human balance in quiet standing and during locomotion. Parkinson’s disease (PD) is the most common degenerative movement disorder that affects human stability and causes falls and injuries. This paper proposes a novel methodology to differentiate between healthy individuals and those with PD through the empirical mode decomposition (EMD) method. EMD enables the breaking down of a complex signal into several elementary signals called intrinsic mode functions (IMFs). Three temporal parameters and three spectral parameters are extracted from each stabilometric signal as well as from its IMFs. Next, the best five features are selected using the feature selection method. The classification task is carried out using four known machine-learning methods, KNN, decision tree, Random Forest and SVM classifiers, over 10-fold cross validation. The used dataset consists of 28 healthy subjects (14 young adults and 14 old adults) and 32 PD patients (12 young adults and 20 old adults). The SVM method has a performance of 92% and the Dempster–Sahfer formalism method has an accuracy of 96.51%.

show abstract

Machine Learning Framework for Implementing Alzheimer’s Disease

Cited by 30 publications

References 17 publications

Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders

Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders

Comparative Analysis of Performance Metrics for Machine Learning Classifiers with a Focus on Alzheimer's Disease Data

EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

Contact Info

Product

Resources

About