An Efficient Classifier for Alzheimer’s Disease Genes Identification

Alzheimer’s disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD.

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A Comparative Study of Alzheimer Detection Techniques

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37882 ◽

2021 ◽

Vol 9 (8) ◽

pp. 2877-2883

Author(s):

Adwait Patil

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Comparative Study ◽

Fuzzy System ◽

Early Stage ◽

Machine Learning Algorithms ◽

Support Vector ◽

Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

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Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer’s Disease With High Performance Computing

Frontiers in Artificial Intelligence ◽

10.3389/frai.2021.798962 ◽

2021 ◽

Vol 4 ◽

Author(s):

Fan Zhang ◽

Melissa Petersen ◽

Leigh Johnson ◽

James Hall ◽

Sid E. O’Bryant

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

High Performance ◽

Support Vector ◽

Massive Datasets ◽

Complexity Of Algorithms ◽

Magnetic Resonance Imaging Mri ◽

Research Challenge ◽

Improved Accuracy

Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer’s disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.

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The Diagnosis of Alzheimer’s Disease: An Ensemble Approach

Fuzzy Systems and Data Mining VI - Frontiers in Artificial Intelligence and Applications ◽

10.3233/faia200689 ◽

2020 ◽

Author(s):

Jingyan Qiu ◽

Linjian Li ◽

Yida Liu ◽

Yingjun Ou ◽

Yubei Lin

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Large Scale ◽

Early Stage ◽

Training Transfer ◽

Detection And Diagnosis ◽

Magnetic Resonance Imaging Mri ◽

Deep Learning Model ◽

The Brain

Alzheimer’s disease (AD) is one of the most common forms of dementia. The early stage of the disease is defined as Mild Cognitive Impairment (MCI). Recent research results have shown the prospect of combining Magnetic Resonance Imaging (MRI) scanning of the brain and deep learning to diagnose AD. However, the CNN deep learning model requires a large scale of samples for training. Transfer learning is the key to enable a model with high accuracy by using limited data for training. In this paper, DenseNet and Inception V4, which were pre-trained on the ImageNet dataset to obtain initialization values of weights, are, respectively, used for the graphic classification task. The ensemble method is employed to enhance the effectiveness and efficiency of the classification models and the result of different models are eventually processed through probability-based fusion. Our experiments were completely conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset. Only the ternary classification is made due to a higher demand for medical detection and diagnosis. The accuracies of AD/MCI/Normal Control (NC) of different models are estimated in this paper. The results of the experiments showed that the accuracies of the method achieved a maximum of 92.65%, which is a remarkable outcome compared with the accuracies of the state-of-the-art methods.

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A Strategy for Identifying the Risk of Developing Alzheimer's Disease From Healthy Controls: Distance to Novelty Detection Boundary

10.21203/rs.3.rs-129454/v1 ◽

2020 ◽

Author(s):

Jiangbing Mao ◽

Qinyong Ye ◽

Hongqing Yang ◽

Magda Bucholc ◽

Shuo Liu ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Cognitive Decline ◽

Early Stage ◽

Novelty Detection ◽

Imaging Biomarkers ◽

Support Vector ◽

Support Vector Data Description ◽

Healthy Controls

Abstract Background:Machine learning (ML) techniques are expected to tackle the problem of the high prevalence of Alzheimer’s disease (AD) we are facing worldwide. However, few studies of novelty detection (ND), a typical ML technique for safety-critical systems especially in healthcare, were engaged for identifying the risk of developing cognitive impairment from healthy controls (HC) population.Materials and Methods: Two independent datasets were used for this study, including the Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) and the Fujian Medical University Union Hospital (FMUUH), China datasets. Multiple feature selection methods were applied to identify the most relevant features for predicting the severity of AD. Four easily interpretable ND algorithms, including k nearest neighbor, Mixture of Gaussian (MoG), KMEANS, and support vector data description were used to construct predictive models. The models were visualized by drawing their decision boundaries tightly surrounding the HC data. A distance to boundary (DtB) strategy was proposed to differentiate individuals with mild cognitive impairment (MCI) and AD from HC. Results: The best overall MCI&AD detection performance in both AIBL and FMUUH was obtained on the cognitive and functional assessments (CFA) modality only using MoG-based ND with AUC of 0.8757 and 0.9443, respectively. The highest sensitivity of MCI was presented by using a combination of CFA and brain imaging modality. The DTB value reflects the risk of developing cognitive impairment for HC and the dementia severity of MCI/AD.Conclusions: Our findings suggest that applying some non-invasive and cost-effective features can significantly detect cognitive decline in an early stage. The visualized decision boundary and the proposed DtB strategy illustrated the severity of cognitive decline of potential MCI&AD patients in an early stage. The results would help inform future guidelines for developing a clinical decision-making support system aiming at an early diagnosis and prognosis of MCI&AD.

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Prediction of Alzheimer’s Disease Using Machine Learning Classifiers

International Electronic Journal of Medicine ◽

10.34172/iejm.2020.21 ◽

2020 ◽

Vol 9 (3) ◽

pp. 116-120

Author(s):

Mansour Rezaei ◽

Ehsan Zereshki ◽

Soodeh Shahsavari ◽

Mohammad Gharib Salehi ◽

Hamid Sharini

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Correct Diagnosis ◽

Medical Science ◽

Screening Tests ◽

Support Vector ◽

Svm Classifier ◽

Rbf Kernel ◽

Magnetic Resonance Imaging Mri ◽

The Brain

Background: Alzheimer’s disease (AD) is the most common brain failure for which no cure has yet been found. The disease starts with a disturbance in the brain structure and then it manifests itself clinically. Therefore, by timely and correct diagnosis of changes in the structure of the brain, the occurrence of this disease or at least its progression can be prevented. Due to the fact that magnetic resonance imaging (MRI) can be used to obtain very useful information from the brain, and also because it is non-invasive, this method has been considered by researchers. Materials and Methods: The data were obtained from an MRI database (MIRIAD) of 69 subjects including 46 AD patients and 23 healthy controls (HC). Individuals were categorized based on two criteria including NINCDS-ADRAD and MMSE, as the gold standard. In this paper, we used the support vector machine (SVM) and Bayesian SVM classifiers. Results: Using the SVM classifier with Gaussian radial basis function (RBF) kernel, we distinguished AD and HC with an accuracy of 88.34%. The most important regions of interest (ROIs) in this study included right para hippocampal gyrus, left para hippocampal gyrus, right hippocampus, and left hippocampus. Conclusion: This study showed that the SVM model with Gaussian RBF kernel can distinguish AD from HC with high accuracy. These studies are of great importance in medical science. Based on the results of this study, MRI centers and neurologists can perform AD screening tests in people over the age of 50 years.

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Diagnosis of Alzheimer’s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/5514839 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Morteza Amini ◽

MirMohsen Pedram ◽

AliReza Moradi ◽

Mahshad Ouchani

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Extraction ◽

Convolutional Neural Network ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Early Stage ◽

Support Vector ◽

Feature Extraction Method

The automatic diagnosis of Alzheimer’s disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer’s disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer’s symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of K -nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer’s severity. The relationship between Alzheimer’s patients’ functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories. Results show that the accuracy of the KNN, SVM, DT, LDA, RF, and presented CNN method is 77.5%, 85.8%, 91.7%, 79.5%, 85.1%, and 96.7%, respectively. Moreover, for the presented CNN architecture, the sensitivity of low, mild, moderate, and severe status of Alzheimer patients is 98.1%, 95.2%,89.0%, and 87.5%, respectively. Based on the findings, the presented CNN architecture classifier outperforms other methods and can diagnose the severity and stages of Alzheimer’s disease with maximum accuracy.

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Identification of Alzheimer’s Disease on the Basis of a Voxel-Wise Approach

Applied Sciences ◽

10.3390/app9153063 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3063

Author(s):

Iman Beheshti ◽

Hadi Mahdipour Hossein-Abad ◽

Hiroshi Matsuda ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Spatial Resolution ◽

Feature Selection Method ◽

Neuronal Loss ◽

Support Vector ◽

Whole Brain ◽

Mri Features ◽

Magnetic Resonance Imaging Mri ◽

The Impact

Robust prediction of Alzheimer’s disease (AD) helps in the early diagnosis of AD and may support the treatment of AD patients. In this study, for early detection of AD and prediction of mild cognitive impairment (MCI) conversion, we develop an automatic computer-aided diagnosis (CAD) framework based on a merit-based feature selection method through a whole-brain voxel-wise analysis using baseline magnetic resonance imaging (MRI) data. We also explore the impact of different MRI spatial resolution on the voxel-wise metric AD classification and MCI conversion prediction. We assessed the proposed CAD framework using the whole-brain voxel-wise MRI features of 507 J-ADNI participants (146 healthy controls [HCs], 102 individuals with stable MCI [sMCI], 112 with progressive MCI [pMCI], and 147 with AD) among four clinically relevant pairs of diagnostic groups at different imaging resolutions (i.e., 2, 4, 8, and 16 mm). Using a support vector machine classifier through a 10-fold cross-validation strategy at a spatial resolution of 2 mm, the proposed CAD framework yielded classification accuracies of 91.13%, 74.77%, 81.12%, and 81.78% in identifying AD/healthy control, sMCI/pMCI, sMCI/AD, and pMCI/HC, respectively. The experimental results show that a lower spatial resolution (i.e., 2 mm) may provide more robust information to trace the neuronal loss-related brain atrophy in AD.

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Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study

Entropy ◽

10.3390/e21100956 ◽

2019 ◽

Vol 21 (10) ◽

pp. 956

Author(s):

Zelong Wang ◽

Majd Abazid ◽

Nesma Houmani ◽

Sonia Garcia-Salicetti ◽

Anne-Sophie Rigaud

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Information Content ◽

Early Stage ◽

A Priori ◽

Support Vector ◽

Linear Discriminant ◽

Linear Classifiers ◽

Online Signature ◽

Time Sequences

We aimed to explore the online signature modality for characterizing early-stage Alzheimer’s disease (AD). A few studies have explored this modality, whereas many on online handwriting have been published. We focused on the analysis of raw temporal functions acquired by the digitizer on signatures produced during a simulated check-filling task. Sample entropy was exploited to measure the information content in raw time sequences. We show that signatures of early-stage AD patients have lower information content than those of healthy persons, especially in the time sequences of pen pressure and pen altitude angle with respect to the tablet. The combination of entropy values on two signatures for each person was classified with two linear classifiers often used in the literature: support vector machine and linear discriminant analysis. The improvements in sensitivity and specificity were significant with respect to the a priori group probabilities in our population of AD patients and healthy subjects. We show that altitude angle, when combined with pen pressure, conveys crucial information on the wrist-hand-finger system during signature production for pathology detection.

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Classification of Alzheimer’s Disease Patients Using Texture Analysis and Machine Learning

Applied System Innovation ◽

10.3390/asi4030049 ◽

2021 ◽

Vol 4 (3) ◽

pp. 49

Author(s):

Sumit Salunkhe ◽

Mrinal Bachute ◽

Shilpa Gite ◽

Nishad Vyas ◽

Saanil Khanna ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Complex Disease ◽

Texture Classification ◽

Region Of Interest ◽

Structural Data ◽

Support Vector ◽

Magnetic Resonance Imaging Mri ◽

The Many ◽

Occurrence Matrix

Alzheimer’s disease (AD) has been studied extensively to understand the nature of this complex disease and address the many research gaps concerning prognosis and diagnosis. Several studies based on structural and textural characteristics have already been conducted to aid in identifying AD patients. In this work, an image processing methodology was used to extract textural information and classify the patients into two groups: AD and Cognitively Normal (CN). The Gray Level Co-occurrence Matrix (GLCM) was employed since it is a strong foundation for texture classification. Various textural parameters derived from the GLCM aided in deciphering the characteristics of a Magnetic Resonance Imaging (MRI) region of interest (ROI). Several commonly used image classification algorithms were employed. MATLAB was used to successfully derive 20 features based on the GLCM of the MRI dataset. Based on the data analysis, 8 of the 20 features were determined as significant elements. Ensemble (90.2%), Decision Trees (88.5%), and Support Vector Machine (SVM) (87.2%) were the best performing classifiers. It was observed in GLCM that as the distance (d) between pixels increased, the classification accuracy decreased. The best result was observed for GLCM with d = 1 and direction (d, d, −d) with age and structural data.

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Support vector machine based alzheimer’s disease diagnosis using synchrony features

International Journal of Informatics and Communication Technology (IJ-ICT) ◽

10.11591/ijict.v9i1.pp57-62 ◽

2020 ◽

Vol 9 (1) ◽

pp. 57

Author(s):

Nilesh Kulkarni

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Support Vector Machine ◽

Early Stage ◽

Research Work ◽

Normal Subjects ◽

Disease Diagnosis ◽

Support Vector ◽

Experimental Database ◽

Wavelet Features

Previous research work has highlighted that neuro-signals of Alzheimer’s disease patients are least complex and have low synchronization as compared to that of healthy and normal subjects. The changes in EEG signals of Alzheimer’s subjects start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet-based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Support Vector Machine classifier is used for classification giving 94% accuracy on experimental database used. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.

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