scholarly journals Early detection algorithm for alzheimer’s disease using autonomous learning

2021 ◽  
Vol 10 (11) ◽  
pp. 608-621
Author(s):  
Jorge Eduardo Aguilar Obregón ◽  
Octavio José Salcedo Parra ◽  
Juan Pablo Rodríguez Miranda
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Nearest Neighbor ◽  
Complete Information ◽  
Research Problem ◽  
Detection Algorithm ◽  
Autonomous Learning ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Information Bank

The current document describes the approach to a research problem that aims to generate an algorithm that allows detecting the probable appearance of Alzheimer’s disease in its first phase, using autonomous learning techniques or Machine Learning, more specifically KNN (K- nearest Neighbor) with which the best result was obtained. This development will be based on a complete information bank taken from ADNI (Alz- heimer’s Disease NeuroImaging Initiative), with all the necessary parameters to direct the inves- tigation to an algorithm that is as efficient as pos- sible, since it has biological, sociodemographic and medical history data, biological specimens, neural images, etc., and in this way the early de- tection of the aforementioned disease was con- figured. A complete guide to the process will be carried out to finally obtain the KNN algorithm whose efficiency is 99%, and then discuss the obtained results. 

scholarly journals Screening for Core Genes Related to Pathogenesis of Alzheimer’s Disease

2021 ◽  
Vol 9 ◽  
Author(s):  
Longxiu Yang ◽  
Yuan Qin ◽  
Chongdong Jian
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Nearest Neighbor ◽  
Core Gene ◽  
Middle Temporal Gyrus ◽  
Support Vector ◽  
Rna Expression ◽  
K Nearest Neighbor ◽  
System Disease ◽  
Core Genes

Alzheimer’s disease (AD), a nervous system disease, lacks effective therapies at present. RNA expression is the basic way to regulate life activities, and identifying related characteristics in AD patients may aid the exploration of AD pathogenesis and treatment. This study developed a classifier that could accurately classify AD patients and healthy people, and then obtained 3 core genes that may be related to the pathogenesis of AD. To this end, RNA expression data of the middle temporal gyrus of AD patients were firstly downloaded from GEO database, and the data were then normalized using limma package following a supplementation of missing data by k-Nearest Neighbor (KNN) algorithm. Afterwards, the top 500 genes of the most feature importance were obtained through Max-Relevance and Min-Redundancy (mRMR) analysis, and based on these genes, a series of AD classifiers were constructed through Support Vector Machine (SVM), Random Forest (RF), and KNN algorithms. Then, the KNN classifier with the highest Matthews correlation coefficient (MCC) value composed of 14 genes in incremental feature selection (IFS) analysis was identified as the best AD classifier. As analyzed, the 14 genes played a pivotal role in determination of AD and may be core genes associated with the pathogenesis of AD. Finally, protein-protein interaction (PPI) network and Random Walk with Restart (RWR) analysis were applied to obtain core gene-associated genes, and key pathways related to AD were further analyzed. Overall, this study contributed to a deeper understanding of AD pathogenesis and provided theoretical guidance for related research and experiments.

Deep Learning for Alzheimer’s Disease Classification using Texture Features

2019 ◽  
Vol 15 (7) ◽  
pp. 689-698 ◽  
Author(s):  
Jae-Hong So ◽  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Boo-Kyeong Choi ◽  
Hyeon-Gyun Park
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Image Data ◽  
Magnetic Resonance Images ◽  
Disease Classification ◽  
Support Vector ◽  
K Nearest Neighbor

Background: We propose a classification method for Alzheimer’s disease (AD) based on the texture of the hippocampus, which is the organ that is most affected by the onset of AD. Methods: We obtained magnetic resonance images (MRIs) of Alzheimer’s patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This dataset consists of image data for AD, mild cognitive impairment (MCI), and normal controls (NCs), classified according to the cognitive condition. In this study, the research methods included image processing, texture analyses, and deep learning. Firstly, images were acquired for texture analyses, which were then re-spaced, registered, and cropped with Gabor filters applied to the resulting image data. In the texture analyses, we applied the 3-dimensional (3D) gray-level co-occurrence (GLCM) method to evaluate the textural features of the image, and used Fisher’s coefficient to select the appropriate features for classification. In the last stage, we implemented a deep learning multi-layer perceptron (MLP) model, which we divided into three types, namely, AD-MCI, AD-NC, and MCI-NC. Results: We used this model to assess the accuracy of the proposed method. The classification accuracy of the proposed deep learning model was confirmed in the cases of AD-MCI (72.5%), ADNC (85%), and MCI-NC (75%). We also evaluated the results obtained using a confusion matrix, support vector machine (SVM), and K-nearest neighbor (KNN) classifier and analyzed the results to objectively verify our model. We obtained the highest accuracy of 85% in the AD-NC. Conclusion: The proposed model was at least 6–19% more accurate than the SVM and KNN classifiers, respectively. Hence, this study confirms the validity and superiority of the proposed method, which can be used as a diagnostic tool for early Alzheimer’s diagnosis.

scholarly journals The Integrated Voxel Analysis Method (IVAM) to Diagnose Onset of Alzheimer’s Disease and Identify Brain Regions through Structural MRI Images

2019 ◽  
Author(s):  
Matthew Hur ◽  
Armen Aghajanyan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Nearest Neighbor ◽  
Feature Vector ◽  
Three Dimensional ◽  
Structural Mri ◽  
Brain Regions ◽  
Brain Atlas ◽  
Analysis Method ◽  
K Nearest Neighbor

AbstractMagnetic Resonance Imaging (MRI) provides three-dimensional anatomical and physiological details of the human brain. We describe the Integrated Voxel Analysis Method (IVAM) which, through machine learning, classifies MRI images of brains afflicted with early Alzheimer’s Disease (AD). This fully automatic method uses an extra trees regressor model in which the feature vector input contains the intensities of voxels, whereby the effect of AD on a single voxel can be predicted. The resulting tree predicts based on the following two steps: a K-nearest neighbor (KNN) algorithm based on Euclidean distance with the feature vector to classify whole images based on their distribution of affected voxels and a voxel-by-voxel classification by the tree of every voxel in the image. An Ising model filter follows voxel-by-voxel tree-classification to remove artifacts and to facilitate clustering of classification results which identify significant voxel clusters affected by AD. We apply this method to T1-weighted MRI images obtained from the Open Access Series of Imaging Studies (OASIS) using images belonging to normal and early AD-afflicted individuals associated with a Client Dementia Rating (CDR) which we use as the target in the supervised learning. Furthermore, statistical analysis using a pre-labeled brain atlas automatically identifies significantly affected brain regions. While achieving 90% AD classification accuracy on 198 images in the OASIS dataset, the method reveals morphological differences caused by the onset of AD.

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease

2020 ◽  
Vol 16 (1s) ◽  
pp. 1-35 ◽  
Author(s):  
M. Tanveer ◽  
B. Richhariya ◽  
R. U. Khan ◽  
A. H. Rashid ◽  
P. Khanna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

scholarly journals Spatiotemporal Analysis of Web News Archives for Crime Prediction

2020 ◽  
Vol 10 (22) ◽  
pp. 8220
Author(s):  
Areeba Umair ◽  
Muhammad Shahzad Sarfraz ◽  
Muhammad Ahmad ◽  
Usman Habib ◽  
Muhammad Habib Ullah ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Language Processing ◽  
Nearest Neighbor ◽  
Spatiotemporal Analysis ◽  
Machine Learning Techniques ◽  
Digital Information ◽  
K Nearest Neighbor ◽  
Digital Devices ◽  
Learning Techniques ◽  
Web News

In today’s world, security is the most prominent aspect which has been given higher priority. Despite the rapid growth and usage of digital devices, lucrative measurement of crimes in under-developing countries is still challenging. In this work, unstructural crime data (900 records) from the news archives of the previous eight years were extracted to predict the behavior of criminals’ networks and transform it into useful information using natural language processing (NLP). To estimate the next move of criminals in Pakistan, we performed hotspot-based spatial analysis. Later, this information is fed to two different classifiers for possible identification and prediction. We achieved the maximum accuracy of 92% using K-Nearest Neighbor (KNN) and 62% using the Random Forest algorithm. In terms of crimes, the results showed that the most prevalent crime events are robberies. Thus, the usage of digital information archives, spatial analysis, and machine learning techniques can open new ways of handling a peaceful and sustainable society in eradicating crimes for countries having paucity of financial resources.

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