Multiple Machine Learning Models for Detection of Alzheimer’s Disease Using OASIS Dataset

Baglat, Preety; Salehi, Ahmad Waleed; Gupta, Ankit; Gupta, Gaurav

doi:10.1007/978-3-030-64849-7_54

Cited by 22 publications

(7 citation statements)

References 16 publications

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“…We compare our proposed RBMSDL model with the existing models using the OASIS dataset for AD binary class classification, as shown in Table 6 . Specifically, the proposed RBMSDL model is compared to models such as deep ensemble [ 27 ], CNN-RFC [ 60 ], Hybrid model [ 61 ], and Multi-ML model [ 62 ] to determine the binary AD-classification task. In the existing methods we investigated, most of the comparable accuracy in Loddo et al [ 27 ] presented a deep ensemble strategy using the same dataset for AD diagnosis and reported an accuracy of 98.51%.…”

Section: Experimental Evalutionmentioning

confidence: 99%

Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

Hassan,

Musa Miah,

Shin

2024

J. Imaging

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Alzheimer’s Disease (AD) poses a significant health risk globally, particularly among the elderly population. Recent studies underscore its prevalence, with over 50% of elderly Japanese facing a lifetime risk of dementia, primarily attributed to AD. As the most prevalent form of dementia, AD gradually erodes brain cells, leading to severe neurological decline. In this scenario, it is important to develop an automatic AD-detection system, and many researchers have been working to develop an AD-detection system by taking advantage of the advancement of deep learning (DL) techniques, which have shown promising results in various domains, including medical image analysis. However, existing approaches for AD detection often suffer from limited performance due to the complexities associated with training hierarchical convolutional neural networks (CNNs). In this paper, we introduce a novel multi-stage deep neural network architecture based on residual functions to address the limitations of existing AD-detection approaches. Inspired by the success of residual networks (ResNets) in image-classification tasks, our proposed system comprises five stages, each explicitly formulated to enhance feature effectiveness while maintaining model depth. Following feature extraction, a deep learning-based feature-selection module is applied to mitigate overfitting, incorporating batch normalization, dropout and fully connected layers. Subsequently, machine learning (ML)-based classification algorithms, including Support Vector Machines (SVM), Random Forest (RF) and SoftMax, are employed for classification tasks. Comprehensive evaluations conducted on three benchmark datasets, namely ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS Kaggle, demonstrate the efficacy of our proposed model. Impressively, our model achieves accuracy rates of 99.47%, 99.10% and 99.70% for ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS datasets, respectively, outperforming existing systems in binary class problems. Our proposed model represents a significant advancement in the AD-analysis domain.

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Section: Experimental Evalutionmentioning

confidence: 99%

Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

Hassan,

Musa Miah,

Shin

2024

J. Imaging

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“…Baglat et al [16] used ML techniques such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost to improve the early diagnosis and classification of Alzheimer's disease in the OASIS MRI dataset, with the RF classifier outperforming the others. The RF classifier's accuracy, recall, and area under coverage values are 86.8%, 80%, and 0.872, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Amrutesh et al[19] used the text data from OASIS dataset and got 92.13% accuracy on RF. Training and testing the RF model on MRI OASIS data Baglat et al[16] and Kavitha et al[18] achieved 86.8% and 86.92% accuracy. The version of OASIS dataset was not mentioned in their work.…”

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confidence: 99%

Explainable AI-based Alzheimer’s prediction and management using multimodal data

Jahan,

Abu Taher,

Kaiser

et al. 2023

PLoS ONE

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Background According to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is rising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data. Objective To solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, MRI segmentation data, and psychological data. However, currently, there is very little understanding of multimodal five-class classification of Alzheimer’s disease. Method For predicting five class classifications, 9 most popular Machine Learning models are used. These models are Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaB), Support Vector Machine (SVM), and Naive Bayes (NB). Among these models RF has scored the highest value. Besides for explainability, SHapley Additive exPlanation (SHAP) is used in this research work. Results and conclusions The performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer’s disease, cognitively normal, non-Alzheimer’s dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer’s disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work.

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“…Decision trees, particularly pruned variants like J48, were applied by Battineni et al [24] to prognosticate late-life AD. Baglat et al [25] tested an assortment of machine learning methodologies on T1-weighted MRI data, with RF and AdaBoost classifiers yielding an accuracy of 86%. Tuan et al [26] proposed a deep learning model tailored for 3D brain MR image segmentation.…”

Section: Related Workmentioning

confidence: 99%

MR Image Feature Analysis for Alzheimer’s Disease Detection Using Machine Learning Approaches

Reza,

Afrin,

Ullah

et al. 2023

IDA

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Alzheimer's disease (AD), a progressive neurological disorder, predominantly impacts cognitive functions, manifesting as memory loss and deteriorating thinking abilities. Recognized as the primary form of dementia, this affliction subtly commences within brain cells and gradually aggravates over time. In 2023, dementia's financial burden for elderly adults aged 65 and older was projected to reach $345 billion, encompassing health care, long-term care, and hospice services. Alarmingly, Alzheimer's disease claims one in three seniors, outnumbering combined fatalities from breast and prostate cancer. Currently, the diagnostic landscape for Alzheimer's lacks definitive tests, and diagnoses based purely on biological definitions have been observed to possess low predictive accuracy. In the presented study, a diagnostic methodology has been proposed using machine learning models that harness image features derived from brain MRI scans. Specifically, nine salient image features, grounded in color, texture, shape, and orientation, were extracted for the study. Four classifiers -Naïve-Bayes, Logistic regression, XGBoost, and AdaBoost -were employed, as the challenge presented a binary classification scenario. A grid search parameter optimization technique was employed to fine-tune model configurations, ensuring optimal predictive outcomes. Conducted experiments utilizing the Kaggle dataset, and for each model, parameters were rigorously optimized. The XGBoost classifier demonstrated superior performance, achieving a test accuracy of 92%, while Naïve Bayes, Logistic Regression, and AdaBoost registered accuracies of 63%, 70%, and 72%, respectively. Relative to contemporary methods, the proposed diagnostic approach exhibits commendable accuracy in predicting AD. If AI-based predictive diagnostics for AD are realized using the strategies delineated in this study, significant benefits may be anticipated for healthcare practitioners.

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Multiple Machine Learning Models for Detection of Alzheimer’s Disease Using OASIS Dataset

Cited by 22 publications

References 16 publications

Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection

Explainable AI-based Alzheimer’s prediction and management using multimodal data

MR Image Feature Analysis for Alzheimer’s Disease Detection Using Machine Learning Approaches

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