Alzheimer’s Disease Diagnosis by using Dimensionality Reduction Based on Knn Classifier

Balamurugan, M.; Nancy, A. Maria; Vijaykumar, Suhasini

doi:10.13005/bpj/1299

Cited by 28 publications

(13 citation statements)

References 9 publications

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“…Despite many analyses and their successful data produced as an outcome, their success rate relies on the way of the collection of samples and their bio-safety practices carried out. The data loss or modified data, errors in data are due to the lack of care on samples which are considered to be simple and easy but their impact affects the analysis as this is highly sensitive process and extraction of the information [27,28]. The pre-analytical characteristics, such as sample processing need to be monitored initially to avoid experimental errors on resultant data [29].…”

Section: Discussionmentioning

confidence: 99%

Sample-a Raw Data for Future Information of Life

Aishwarya¹,

Selvam

Nancy

2019

Int J Pharm Pharm Sci

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Gene amplification requires a biological sample which is collected prior to the demands. The sample for such analysis plays a vital role as they serve as a resource for the core material-Deoxyribonucleic acid. A sample for gene amplification or any analysis would be collected prior to the demands and type of analysis. Human blood has been an essential resource of DNA from the commencement of DNA extraction in the 19th century. After then several protocols developed according to the requirement of both samples of different forms and their retrieval as various forms and methods. The sample has various characteristics and prerequisites when collected for gene analysis. Some important characteristics of sample collection methods are significant, which are not widely taken. This work analyses various general sample collection protocols and highlights some of the major characteristics and prerequisites for the sample. This work initiates and delivers to attain the core of genomics by bringing out the basic raw materials importance and consequences on amplification which is highly sensitive.

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Section: Discussionmentioning

confidence: 99%

Sample-a Raw Data for Future Information of Life

Aishwarya¹,

Selvam

Nancy

2019

Int J Pharm Pharm Sci

Self Cite

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“…Executing malaria vector data analysis in data mining system by utilizing classification algorithms requires, getting the evaluation measures requires the output results of a classification confusion matrix, which comprises of the metrics used in evaluating the classified models, where the model predicts the classes and outcomes (True Positive TP, True Negative TN, False Positive FP, and False Negative) [41]:…”

Section: Evaluation Measuresmentioning

confidence: 99%

A survey of dimension reduction and classification methods for RNA-Seq data on malaria vector

et al. 2021

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Recently unique spans of genetic data are produced by researchers, there is a trend in genetic exploration using machine learning integrated analysis and virtual combination of adaptive data into the solution of classification problems. Detection of ailments and infections at early stage is of key concern and a huge challenge for researchers in the field of machine learning classification and bioinformatics. Considerate genes contributing to diseases are of huge dispute to a lot of researchers. This study reviews various works on Dimensionality reduction techniques for reducing sets of features that groups data effectively with less computational processing time and classification methods that contributes to the advances of RNA-Sequencing approach.

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“…Recognition methods have been developed into computer-aided systems, which are used in different types of biomarker sensing imaging to diagnose dementia, (2,3) especially in MRI and PET examinations. Balamurugan et al (4) proposed a novel dimensionality-reduction-based k-nearest-neighbor classification algorithm for analyzing and classifying AD. Dinu and Ganesan (5) introduced an instance-based k-nearest-neighbor classifier using the T-test method for joint regression and classification for early detection of AD.…”

Section: Introductionmentioning

confidence: 99%

Using Three-dimensional Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

Lin¹,

Lin²

2021

Sensors and Materials

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Alzheimer's disease (AD) is an irreversible neurodegenerative disease. Pathology shows atrophy of brain tissue, senile plaques, neurofibrillary tangles, and so forth. Magnetic resonance imaging (MRI) is the most sensitive brain imaging method in a clinic, which provides detailed anatomical structure information of the brain and is commonly studied with pattern recognition methods for AD diagnosis. Most existing methods extract hand-crafted imaging features or brain region-of-interest images to train a classifier to recognize AD. In this study, a threedimensional convolutional neural network (3D-CNN) is implemented to detect AD. The proposed network is trained with 3D magnetic resonance (MR) images to extract the spatial features. A uniform experimental design is used to optimize the network parameters and improve the 3D-CNN performance. To ensure satisfactory performance on a small amount of training data, a transfer learning technology is proposed to improve the recognition rate of the 3D-CNN. In addition, the uniform experimental design (UED) method is used to determine the optimal parameter combination of the network and improve the 3D-CNN performance. The validation data in this study are from the Open Access Series of Imaging Studies (OASIS), where the OASIS-1 data set is used as the original data set and the 3D-CNN is trained as the pretraining model. Experimental results show that when 10, 30, 60, and 90% of the OASIS-2 data set are used to train the pre-trained 3D-CNN, the average accuracy reaches 74.66, 86.99, 94.58, and 97.02%, respectively. In addition, compared with the original manual design parameters, the proposed 3D-CNN with the best parameter combination improves the recognition rate by 2.07 percentage points.

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Alzheimer’s Disease Diagnosis by using Dimensionality Reduction Based on Knn Classifier

Cited by 28 publications

References 9 publications

Sample-a Raw Data for Future Information of Life

Sample-a Raw Data for Future Information of Life

A survey of dimension reduction and classification methods for RNA-Seq data on malaria vector

Using Three-dimensional Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

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