scholarly journals Improved Reasoning with Uncertainty Based Significant Feature Subset Selection for Alzheimer’s Disease Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 3123-3131
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Significant Feature ◽  
Feature Subset ◽  
Shafer Theory

In this modern era neurodegenerative disorder of undefined causes affects the older adults and it becomes most cause of dementia. The Alzheimer’s disease is one of such neurodegenerative disorder which is very complex and hard to predict in the early stage. With evolving advancement in the field of machine learning, it is possible to predict the early stage of AD and diagnosing in initial stages may produce effect result for their further quality and healthy life. But uncertainty in determination of Alzheimer’s is a toughest challenge for the researchers in the field of machine learning. This paper aims to overcome the uncertainty in discovering dementia and non-dementia victims of Alzheimer’s by devising an improved reasoning with uncertainty based prominent feature subset selection using modified fuzzy dempster shafer theory (IRU-DST). For Alzheimer’s disease prediction the dataset is used form OASIS dataset. The performance of the proposed IRU-DST is validated using fuzzy artificial neural network. The simulation results proved the performance of the IRU-DST achieves better results comparing the other sate of arts, by gaining high accuracy rate and it also minimize the error rate considerably with the ability of handling uncertainty.

A Comparative Study of Alzheimer Detection Techniques

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

scholarly journals Bioinformatics analysis of differentially expressed genes and identification of an miRNA–mRNA network associated with entorhinal cortex and hippocampus in Alzheimer’s disease

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Haoming Li ◽  
Linqing Zou ◽  
Jinhong Shi ◽  
Xiao Han
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differentially Expressed Genes ◽  
Entorhinal Cortex ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Differentially Expressed ◽  
Hub Genes

Abstract Background Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, and the lesions originate in the entorhinal cortex (EC) and hippocampus (HIP) at the early stage of AD progression. Gaining insight into the molecular mechanisms underlying AD is critical for the diagnosis and treatment of this disorder. Recent discoveries have uncovered the essential roles of microRNAs (miRNAs) in aging and have identified the potential of miRNAs serving as biomarkers in AD diagnosis. Methods We sought to apply bioinformatics tools to investigate microarray profiles and characterize differentially expressed genes (DEGs) in both EC and HIP and identify specific candidate genes and pathways that might be implicated in AD for further analysis. Furthermore, we considered that DEGs might be dysregulated by miRNAs. Therefore, we investigated patients with AD and healthy controls by studying the gene profiling of their brain and blood samples to identify AD-related DEGs, differentially expressed miRNAs (DEmiRNAs), along with gene ontology (GO) analysis, KEGG pathway analysis, and construction of an AD-specific miRNA–mRNA interaction network. Results Our analysis identified 10 key hub genes in the EC and HIP of patients with AD, and these hub genes were focused on energy metabolism, suggesting that metabolic dyshomeostasis contributed to the progression of the early AD pathology. Moreover, after the construction of an miRNA–mRNA network, we identified 9 blood-related DEmiRNAs, which regulated 10 target genes in the KEGG pathway. Conclusions Our findings indicated these DEmiRNAs having the potential to act as diagnostic biomarkers at an early stage of AD.

scholarly journals A Machine Learning Approach to Unmask Novel Gene Signatures and Prediction of Alzheimer’s Disease Within Different Brain Regions

2021 ◽  
Author(s):  
Abhibhav Sharma ◽  
Pinki Dey
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Non Coding Rna ◽  
Machine Learning Approach ◽  
Microarray Datasets ◽  
Rna Genes

AbstractAlzheimer’s disease (AD) is a progressive neurodegenerative disorder whose aetiology is currently unknown. Although numerous studies have attempted to identify the genetic risk factor(s) of AD, the interpretability and/or the prediction accuracies achieved by these studies remained unsatisfactory, reducing their clinical significance. Here, we employ the ensemble of random-forest and regularized regression model (LASSO) to the AD-associated microarray datasets from four brain regions - Prefrontal cortex, Middle temporal gyrus, Hippocampus, and Entorhinal cortex- to discover novel genetic biomarkers through a machine learning-based feature-selection classification scheme. The proposed scheme unrevealed the most optimum and biologically significant classifiers within each brain region, which achieved by far the highest prediction accuracy of AD in 5-fold cross-validation (99% average). Interestingly, along with the novel and prominent biomarkers including CORO1C, SLC25A46, RAE1, ANKIB1, CRLF3, PDYN, numerous non-coding RNA genes were also observed as discriminator, of which AK057435 and BC037880 are uncharacterized long non-coding RNA genes.

scholarly journals Revisiting the Amyloid Cascade Hypothesis: From Anti-Aβ Therapeutics to Auspicious New Ways for Alzheimer’s Disease

2020 ◽  
Vol 21 (16) ◽  
pp. 5858 ◽  
Author(s):  
Md. Sahab Uddin ◽  
Md. Tanvir Kabir ◽  
Md. Sohanur Rahman ◽  
Tapan Behl ◽  
Philippe Jeandet ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Clinical Symptoms ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Amyloid Β ◽  
Aβ Oligomers ◽  
Aβ Aggregation ◽  
Clinical Benefits

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder related to age, characterized by the cerebral deposition of fibrils, which are made from the amyloid-β (Aβ), a peptide of 40–42 amino acids. The conversion of Aβ into neurotoxic oligomeric, fibrillar, and protofibrillar assemblies is supposed to be the main pathological event in AD. After Aβ accumulation, the clinical symptoms fall out predominantly due to the deficient brain clearance of the peptide. For several years, researchers have attempted to decline the Aβ monomer, oligomer, and aggregate levels, as well as plaques, employing agents that facilitate the reduction of Aβ and antagonize Aβ aggregation, or raise Aβ clearance from brain. Unluckily, broad clinical trials with mild to moderate AD participants have shown that these approaches were unsuccessful. Several clinical trials are running involving patients whose disease is at an early stage, but the preliminary outcomes are not clinically impressive. Many studies have been conducted against oligomers of Aβ which are the utmost neurotoxic molecular species. Trials with monoclonal antibodies directed against Aβ oligomers have exhibited exciting findings. Nevertheless, Aβ oligomers maintain equivalent states in both monomeric and aggregation forms; so, previously administered drugs that precisely decrease Aβ monomer or Aβ plaques ought to have displayed valuable clinical benefits. In this article, Aβ-based therapeutic strategies are discussed and several promising new ways to fight against AD are appraised.

scholarly journals Mitochondrial Deficits With Neural and Social Damage in Early-Stage Alzheimer’s Disease Model Mice

2021 ◽  
Vol 13 ◽  
Author(s):  
Afzal Misrani ◽  
Sidra Tabassum ◽  
Qingwei Huo ◽  
Sumaiya Tabassum ◽  
Jinxiang Jiang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Mitochondrial Dynamics ◽  
Mitochondrial Fission ◽  
Neuronal Morphology ◽  
Therapeutic Interventions ◽  
Early Event ◽  
Mitochondrial Morphology

Alzheimer’s disease (AD) is the most common neurodegenerative disorder worldwide. Mitochondrial dysfunction is thought to be an early event in the onset and progression of AD; however, the precise underlying mechanisms remain unclear. In this study, we investigated mitochondrial proteins involved in organelle dynamics, morphology and energy production in the medial prefrontal cortex (mPFC) and hippocampus (HIPP) of young (1∼2 months), adult (4∼5 months) and aged (9∼10, 12∼18 months) APP/PS1 mice. We observed increased levels of mitochondrial fission protein, Drp1, and decreased levels of ATP synthase subunit, ATP5A, leading to abnormal mitochondrial morphology, increased oxidative stress, glial activation, apoptosis, and altered neuronal morphology as early as 4∼5 months of age in APP/PS1 mice. Electrophysiological recordings revealed abnormal miniature excitatory postsynaptic current in the mPFC together with a minor connectivity change between the mPFC and HIPP, correlating with social deficits. These results suggest that abnormal mitochondrial dynamics, which worsen with disease progression, could be a biomarker of early-stage AD. Therapeutic interventions that improve mitochondrial function thus represent a promising approach for slowing the progression or delaying the onset of AD.

scholarly journals Comparison of machine learning approaches for enhancing Alzheimer’s disease classification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10549
Author(s):  
Qi Li ◽  
Mary Qu Yang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Spatial Information ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Disease Classification ◽  
Support Vector ◽  
The Brain

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

Intelligent Feature Subset Selection with Machine Learning based Risk Management for DAS Prediction

2021 ◽  
pp. 08-16
Author(s):  
Mohamed Abdel Abdel-Basset ◽  
◽  
◽  
Mohamed Elhoseny
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Feature Selection ◽  
Subset Selection ◽  
Classification Problem ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Time Machine ◽  
Primary Level ◽  
Stage Process

In the current epidemic situations, people are facing several mental disorders related to Depression, Anxiety, and Stress (DAS). Numerous scales are developed for computing the levels for DAS, and DAS-21 is one among them. At the same time, machine learning (ML) models are applied widely to resolve the classification problem efficiently, and feature selection (FS) approaches can be designed to improve the classifier results. In this aspect, this paper develops an intelligent feature selection with ML-based risk management (IFSML-RM) for DAS prediction. The IFSML-RM technique follows a two-stage process: quantum elephant herd optimization-based FS (QEHO-FS) and decision tree (DT) based classification. The QEHO algorithm utilizes the input data to select a valuable subset of features at the primary level. Then, the chosen features are fed into the DT classifier to determine the existence or non-existence of DAS. A detailed experimentation process is carried out on the benchmark dataset, and the experimental results showcased the betterment of the IFSML-RM technique in terms of different performance measures.

scholarly journals Supervised Machine Learning Algorithms For Early Diagnosis Of Alzheimer’s Disease

2019 ◽  
Vol 8 (3) ◽  
pp. 7964-7967
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Memory Loss ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Diagnostic Tools ◽  
Longitudinal Mri ◽  
Permanent Brain Damage

Alzheimer’s is a neurodegenerative disease which can eventually leads to dementia. Mostly occurring in elderly people over the age of 65, it is hard to detect and diagnose correctly. Most common symptoms include memory loss and slow deterioration of cognitive functions. Given that these symptoms are seen often in old people, this hinders the detection of Alzheimer’s disease (AD). Alzheimer’s is currently incurable, but detection of the disease during its early stage is often beneficial to the patient, since there are treatments which can considerably improve the quality of life of the patient. However this can only be done if the patient has been diagnosed at a stage before any permanent brain damage has been done. Most of the current methods for detecting and diagnosing AD are not good enough. It is the need of the hour to develop better and early diagnostic tools. With the improvements in the field of machine learning, we now have the tools needed to drastically improve detection of Alzheimer’s. We examine various machine learning methods and algorithms to find a method which can boost the chances of detecting the disease. We will use the following algorithms: Decision Tree, SVM, Random Forest and Adaboost. The dataset being used is the longitudinal MRI data available included in the OASIS dataset. We will use the aforementioned algorithms on the dataset and compare the accuracies achieved to find an optimal.

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