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Alzheimer’s disease (AD) is the most common type of neurological disorder that leads to the brain’s cell death overtime. It is one of the major important causes of memory loss and cognitive decline in elderly subjects around the globe. Early detection and streamlining of diagnostic practices are the prime domains of the interest to the healthcare c...
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... where it can be inferred that Age, EDUC, SES, MMSE, eTIV, and nWBV feature columns exhibit outliers while other feature columns are outlier-free. 2. Determining Skewness: The linearity of the features was ascertained by plotting a distribution plot. The plot was used to study the skewness of both the dependent and independent variables. From the Fig. 4, it can be concluded that Group, M/F, Age feature columns appear to be normally distributed while EDUC, SES, MMSE, CDR, eTIV, nWBV, and ASF all independent variables experience skewness. 3. Discovering Structural Correlation: It is determined by scatter-plot matrix which is used to plot the multiple pairwise bivariate distributions of ...
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Objective:
To contribute to the implementation of screening strategies for cognitive decline and memory deficits in patients with epilepsy.
Methods:
Two questionnaires, MMSE and MoCA, were used in this cross-sectional and observational study. Fifty-four patients diagnosed with different types of epilepsy (55% refractory) were assessed; they were a...
Citations
... These systems will ultimately help speed up the treatment of patients with AD. To make predictions, scientists have adopted various ML-based classifiers, including support vector machines (SVMs) [8,9], hidden Markov models [10,11], k-nearest neighbors classifier [12,13], discriminant analysis [14,15], random forest [16,17], decision trees [18], naive Bayes classifier [19,20], and artificial neural networks (ANNs) [21,22]. ...
... For the OASIS dataset, the PCA/SVM method had a 91.9% accuracy and the ViT model with data augmentation had a 93.2% accuracy. Nanni et al [33], Khan and Zubair [16], Sethi et al [2], Basheer et al [34], Saratxaga et al [35], and Liu et al [36] got 90.2%, 86.8%, 86.2%, 92.3%, 93%, and 82.6% accuracy, respectively. ...
Background
Alzheimer disease (AD) is a severe neurological brain disorder. While not curable, earlier detection can help improve symptoms substantially. Machine learning (ML) models are popular and well suited for medical image processing tasks such as computer-aided diagnosis. These techniques can improve the process for an accurate diagnosis of AD.
Objective
In this paper, a complete computer-aided diagnosis system for the diagnosis of AD has been presented. We investigate the performance of some of the most used ML techniques for AD detection and classification using neuroimages from the Open Access Series of Imaging Studies (OASIS) and Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets.
Methods
The system uses artificial neural networks (ANNs) and support vector machines (SVMs) as classifiers, and dimensionality reduction techniques as feature extractors. To retrieve features from the neuroimages, we used principal component analysis (PCA), linear discriminant analysis, and t-distributed stochastic neighbor embedding. These features are fed into feedforward neural networks (FFNNs) and SVM-based ML classifiers. Furthermore, we applied the vision transformer (ViT)–based ANNs in conjunction with data augmentation to distinguish patients with AD from healthy controls.
Results
Experiments were performed on magnetic resonance imaging and positron emission tomography scans. The OASIS dataset included a total of 300 patients, while the ADNI dataset included 231 patients. For OASIS, 90 (30%) patients were healthy and 210 (70%) were severely impaired by AD. Likewise for the ADNI database, a total of 149 (64.5%) patients with AD were detected and 82 (35.5%) patients were used as healthy controls. An important difference was established between healthy patients and patients with AD ( P =.02). We examined the effectiveness of the three feature extractors and classifiers using 5-fold cross-validation and confusion matrix–based standard classification metrics, namely, accuracy, sensitivity, specificity, precision, F 1 -score, and area under the receiver operating characteristic curve (AUROC). Compared with the state-of-the-art performing methods, the success rate was satisfactory for all the created ML models, but SVM and FFNN performed best with the PCA extractor, while the ViT classifier performed best with more data. The data augmentation/ViT approach worked better overall, achieving accuracies of 93.2% (sensitivity=87.2, specificity=90.5, precision=87.6, F 1 -score=88.7, and AUROC=92) for OASIS and 90.4% (sensitivity=85.4, specificity=88.6, precision=86.9, F 1 -score=88, and AUROC=90) for ADNI.
Conclusions
Effective ML models using neuroimaging data could help physicians working on AD diagnosis and will assist them in prescribing timely treatment to patients with AD. Good results were obtained on the OASIS and ADNI datasets with all the proposed classifiers, namely, SVM, FFNN, and ViTs. However, the results show that the ViT model is much better at predicting AD than the other models when a sufficient amount of data are available to perform the training. This highlights that the data augmentation process could impact the overall performance of the ViT model.
... Probably, these numbers will continue to increase. Given the rising public health importance of AD, the analysis of such data indicates an immediate demand for decisive measures [7]. ...
Neurodegenerative disorders such as dementia and Alzheimer’s disease (AD) have adversely devastated the health and well-being of the older community. Given that early detection might help prevent or delay cognitive disorders associated with AD, developing an effective diagnostic technique is deemed appropriate to control the disease. Despite advances in clinical diagnostic standards and treatment techniques, the global prevalence of cognitive disturbance and behavioral problems remains steep. Besides the above challenge, the scarcity of AD-related open-source raw data prompted us to tailor ADNeuroNet, a neuroevolution-based neural network (NN) designed for predicting cognitively normal, mild cognitive impairment, and AD instances. To construct a robust NN algorithm that can predict the disease with high accuracy and improved efficiency, we worked on the combined cognitive and demographic clinical data to create a comprehensive model. Primarily, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) repository was used to develop this program. We employed three distinct datasets and developed a predictive model that achieved the maximum performance accuracy of 93.42%. Utilizing only baseline details, the as-developed model successfully diagnosed and predicted neurodegenerative disorders and is likely to emerge as an effective clinical tool in future endeavors.
... Radio recurrence (RF) transmitter framework schematics and reproduction in Agilent Progressed Plan Framework (Promotions) were used in the gearbox plan. The study recommended the precision of random designs and provided simulation methodologies to test their viability as well as diagnostic tools (Khan & Zubair, 2020). Thanks to a study of carbon nanotube (CNT) patch antennas, the design described in the paper improved antenna metrics like VSWR, return loss, gain, radiation pattern, and efficient energy harvesting (Patel & Shah, 2021). ...
... D-B scan, another clustering approach for organising unlabeled data, is presented in (Mahdavinejad, Rezvan, & Burcins, 2018). It's also used to group together little citizens in (Khan & Zubair, 2020). ...
... Recent studies have presented a number of indicators of comprehensive detection and monitoring by using a totally automated AD monitoring report. Furthermore, the system separates among great and undesirable people (Khan & Zubair, 2020). The infrastructure of healthcare systems cannot function without the use of remote healthcare monitoring. ...
Evolution of AI in the medical field is the biggest challenge. Research organisations are committed to continuing the in-depth quest for intelligence because of specific long-term demands and challenges in medicine. Because of developments in areas like the internet of things, cloud computing, and 5G mobile networks, artificial intelligence (AI) technology is being used in healthcare. Additionally, improved public services are made possible by the extensive integration of IoT technology and artificial intelligence, which gradually enhances diagnostic and therapeutic capabilities. The authors join the thoughts behind unambiguous calculations like to portray situation-based applications like distant conclusion and clinical coordination, pediatric escalated care units, cardiovascular concentrated care units, trauma centers, venous thromboembolism, patient consideration, and imaging utilizing the web of things (IoT), the cloud, huge information examination, and AI in medical services.
... The authors demonstrated the DL techniques' potential within the domain of AD diagnosis. A sophisticated approach for the detection of AD using multi-modal ML was presented by Khan et al. [16]. Here, they proposed a five-stage ML pipeline that incorporates data transformation and feature selection along with a random forest classifier. ...
Alzheimer’s disease (AD) is the most common neurodegeneration, annually diagnosed in millions of patients. The present medicine scenario still finds challenges in the exact diagnosis and classification of AD through neuroimaging data. Traditional CNNs can extract a good amount of low-level information in an image but fail to extract high-level minuscule particles, which is a significant challenge in detecting AD from MRI scans. To overcome this, we propose a novel Granular Feature Integration method to combine information extraction at different scales combined with an efficient information flow, enabling the model to capture both broad and fine-grained features simultaneously. We also propose a Bi-Focal Perspective mechanism to highlight the subtle neurofibrillary tangles and amyloid plaques in the MRI scans, ensuring that critical pathological markers are accurately identified. Our model achieved an F1-Score of 99.31%, precision of 99.24%, and recall of 99.51%. These scores prove that our model is significantly better than the state-of-the-art (SOTA) CNNs in existence.
... 1,Online First Online First baseline neuropsychological assessments, genetic data, and MRI images. Khan and Zubair [14] proposed an automated classification system based on Random Forest (RF) to classify AD patients into five distinct stages using MRI images and neuropsychological assessments. Almubark et al. [15] compared the performance of various machine learning algorithms, including Fully Connected Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB) classifiers, using behavioral data derived from neuropsychological assessments and/or cognitive tasks. ...
Alzheimer’s disease (AD) is a gradient degeneration of essential cognitive activities such as memory, thinking, and cognition. AD mainly affects elderly individuals and is recognized as the most common cause of dementia. This study investigates the predictive performance of nine supervised machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Gaussian Naïve Bayes, Multi-Layer Perceptron, eXtreme Gradient Boost, and Gradient Boosting—using neuropsychological assessment data. We applied two classification techniques—binary and multiclass—to classify 1761 subjects into three categories: cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD). Binary classification tasks focused on CNvsAD and CNvsMCI subsets, while multiclass classification used the full dataset (TriClass). Hyperparameter tuning was performed to optimize model performance. The results indicate that ensemble learning models, particularly Gradient Boosting (GB) and Random Forest (RF), exhibited superior accuracy compared to other algorithms. Most models for the CNvsAD subset achieved the highest accuracy (97.74%), while GB achieved the best performance (94.98%) for the CNvsMCI subset. For multiclass classification, RF achieved the highest accuracy at 84.70%. These findings highlight the robustness and efficiency of ensemble learning algorithms, especially in handling complex, non-linear data structures. This study underscores the potential of RF and GB as reliable tools for early detection and classification of Alzheimer’s disease using neuropsychological data.
... We have observed that most of the aforementioned state-of-the-art models [5][6][7][8][9][10][11][12][13][14] struggle to generalize across different MRI datasets for Alzheimer's detection. These models particularly exhibit overfitting when dealing with imbalanced and small datasets. ...
Alzheimer's Disease (AD) is a non-curable progressive neurodegenerative disorder that affects the human brain, leading to a decline in memory, cognitive abilities, and eventually, the ability to carry out daily tasks. Manual diagnosis of Alzheimer's disease from MRI images is fraught with less sensitivity and it is a very tedious process for neurologists. Therefore, there is a need for an automatic Computer Assisted Diagnosis (CAD) system, which can detect AD at early stages with higher accuracy. In this research, we have proposed a novel AD-Lite Net model (trained from scratch), that could alleviate the aforementioned problem. The novelties we bring here in this research are, (I) We have proposed a very lightweight CNN model by incorporating Depth Wise Separable Convolutional (DWSC) layers and Global Average Pooling (GAP) layers. (II) We have leveraged a ``parallel concatenation block'' (pcb), in the proposed AD-Lite Net model. This pcb consists of a Transformation layer (Tx-layer), followed by two convolutional layers, which are thereby concatenated with the original base model. This Tx-layer converts the features into very distinct kind of features, which are imperative for the Alzheimer's disease. As a consequence, the proposed AD-Lite Net model with ``parallel concatenation'' converges faster and automatically mitigates the class imbalance problem from the MRI datasets in a very generalized way. For the validity of our proposed model, we have implemented it on three different MRI datasets. Furthermore, we have combined the ADNI and AD datasets and subsequently performed a 10-fold cross-validation experiment to verify the model's generalization ability. Extensive experimental results showed that our proposed model has outperformed all the existing CNN models, and one recent trend Vision Transformer (ViT) model by a significant margin.
... Data reduction methodologies play a significant role in the analysis of reduced datasets while maintaining the integrity of the original data (Khan and Zubair, 2022a). This approach is often used to enhance efficiency, streamline analysis, and effectively manage large datasets (Maharana et al., 2022). ...
... The objective of this study was to construct such a model that can exhibit optimal generalized performance rather than only for the cases used during training. Consequently, cross-validation gives an estimate of the overall performance for each hyperparameter configuration (Khan and Zubair, 2022a). To achieve this, the train data was divided into 5 folds. ...
... Hyperparameters are essential for structuring ML models and are not learned from the data during training. Hyperparameter optimization is the process of systematically searching for the best combination of hyperparameter values to achieve optimal performance from the model (Yang and Shami, 2020;Khan and Zubair, 2022a). It is crucial to optimize these hyperparameters to obtain the best possible results when applied to unseen instances. ...
Introduction
Machine learning (ML) algorithms and statistical modeling offer a potential solution to offset the challenge of diagnosing early Alzheimer's disease (AD) by leveraging multiple data sources and combining information on neuropsychological, genetic, and biomarker indicators. Among others, statistical models are a promising tool to enhance the clinical detection of early AD. In the present study, early AD was diagnosed by taking into account characteristics related to whether or not a patient was taking specific drugs and a significant protein as a predictor of Amyloid-Beta (Aβ), tau, and ptau [AT(N)] levels among participants.
Methods
In this study, the optimization of predictive models for the diagnosis of AD pathologies was carried out using a set of baseline features. The model performance was improved by incorporating additional variables associated with patient drugs and protein biomarkers into the model. The diagnostic group consisted of five categories (cognitively normal, significant subjective memory concern, early mildly cognitively impaired, late mildly cognitively impaired, and AD), resulting in a multinomial classification challenge. In particular, we examined the relationship between AD diagnosis and the use of various drugs (calcium and vitamin D supplements, blood-thinning drugs, cholesterol-lowering drugs, and cognitive drugs). We propose a hybrid-clinical model that runs multiple ML models in parallel and then takes the majority's votes, enhancing the accuracy. We also assessed the significance of three cerebrospinal fluid biomarkers, Aβ, tau, and ptau in the diagnosis of AD. We proposed that a hybrid-clinical model be used to simulate the MRI-based data, with five diagnostic groups of individuals, with further refinement that includes preclinical characteristics of the disorder. The proposed design builds a Meta-Model for four different sets of criteria. The set criteria are as follows: to diagnose from baseline features, baseline and drug features, baseline and protein features, and baseline, drug and protein features.
Results
We were able to attain a maximum accuracy of 97.60% for baseline and protein data. We observed that the constructed model functioned effectively when all five drugs were included and when any single drug was used to diagnose the response variable. Interestingly, the constructed Meta-Model worked well when all three protein biomarkers were included, as well as when a single protein biomarker was utilized to diagnose the response variable.
Discussion
It is noteworthy that we aimed to construct a pipeline design that incorporates comprehensive methodologies to detect Alzheimer's over wide-ranging input values and variables in the current study. Thus, the model that we developed could be used by clinicians and medical experts to advance Alzheimer's diagnosis and as a starting point for future research into AD and other neurodegenerative syndromes.
... In (Khan and Zubair, 2022), the authors used data from the open access imaging studies series OASIS database of MRI brain images for analysis. The dataset consists of 343 samples of MRI sessions involving 150 subjects. ...
Researchers have increasingly focused on applying machine learning algorithms to enhance healthcare operations in the past few years. Machine learning has become increasingly popular and has shown to be a viable strategy for raising the standard of healthcare, preventing disease transmission and early disease detection, reducing hospital operational expenses, aiding government healthcare programs, and enhancing healthcare efficiency. This review offers a succinct and well-structured summary of machine learning research that has been done in the field of healthcare. Specifically, the emphasis is placed on the examination of non-communicable illnesses, which pose a significant risk to public health and rank among the primary contributors to global mortality. Moreover, the COVID-19 pandemic, which is among the world's deadliest illnesses and has recently been formally declared a public health emergency, is included. This study aims to assist health sector researchers in choosing appropriate algorithms. After conducting a comprehensive investigation, it was shown that the Decision Tree (DT), Gaussian Naive Bayes (GNB), and Random Forest (RF), algorithms had the highest performance in healthcare classification, achieving a remarkable accuracy rate of 100%. In most tests, the Random Forest (RF) and Support Vector Machine (SVM) demonstrated consistently better performance
... showcase the potential of DL techniques for AD diagnosis, offering promising avenues for further research and clinical application in health informatics. Khan et al. [16] proposed an advanced multi-modal ML approach for AD prognosis. They introduced a five-stage ML pipeline integrating data transformation, feature selection, and a random forest classifier. ...
Alzheimer's disease (AD) represents the primary form of neurodegeneration, impacting millions of individuals each year and causing progressive cognitive decline. Accurately diagnosing and classifying AD using neuroimaging data presents ongoing challenges in medicine, necessitating advanced interventions that will enhance treatment measures. In this research, we introduce a dual attention enhanced deep learning (DL) framework for classifying AD from neuroimaging data. Combined spatial and self-attention mechanisms play a vital role in emphasizing focus on neurofibrillary tangles and amyloid plaques from the MRI images, which are difficult to discern with regular imaging techniques. Results demonstrate that our model yielded remarkable performance in comparison to existing state of the art (SOTA) convolutional neural networks (CNNs), with an accuracy of 99.1%. Moreover, it recorded remarkable metrics, with an F1-Score of 99.31%, a precision of 99.24%, and a recall of 99.5%. These results highlight the promise of cutting edge DL methods in medical diagnostics, contributing to highly reliable and more efficient healthcare solutions.
... They used multimodal neuroimaging MRI and cognitive tests for the classification purpose [44]. Several studies are conducted by the researchers on the Open Access Series Imaging Studies (OASIS) dataset [45][46][47][48]. Convolutional Neural Networks (CNN) are used for the detection of dementia from MRI images [45]. ...
... An ensemble of Deep CNN's are used for the detection of Alzheimer's Disease (AD) [46]. Studies are also conducted on the multimodal OASIS data [47][48][49][50]. Several ML algorithms are experimented and the Random Forest (RF) algorithm has reported with a better performance [47]. ...
... Studies are also conducted on the multimodal OASIS data [47][48][49][50]. Several ML algorithms are experimented and the Random Forest (RF) algorithm has reported with a better performance [47]. Further, a Support Vector Machine (SVM) is also used for the prediction of dementia [48]. ...
Early detection of dementia patients in advance is a great concern for the physicians. That is why physicians make use of multi modal data to accomplish this. The baseline visit data of the patients are mainly utilized for this task. Modern Machine Learning techniques provide empirical evidence based approach to physicians for predicting the diagnosis status of the patients. This paper proposes an ensemble majority voting classifier approach for improving the detection of dementia using baseline visit data. The ensemble model consists of Logistic Regression, Random Forest, and Naive Bayes Classifiers. The proposed ensemble classifier reported with a BCA, F1-score of 92%, 0.92 for classifying demented and non-demented patients. Our results suggest that the prediction using the ensemble majority voting classifier improves the Balanced Classification Accuracy, F1-score for predicting dementia on the multi modal data of Open Access Series Imaging Dataset. The results using ensemble models are promising and highlight the importance of using ensemble models for dementia detection using multimodal data.
