A Proposed Alzheimer's Disease Diagnosing System Based on Clustering and Segmentation Techniques

Mohammed, Sarah Jasim

doi:10.30684/etj.36.2b.12

Cited by 3 publications

(2 citation statements)

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“…Mohammed [7] presented a technique based on machine learning (SVM). To find AD genes that are prevalent across the entire genome, the proposed technique was tested by merging gene expression data using data from a specific gene network inside the human brain.…”

Section: Introductionmentioning

confidence: 99%

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Mohammed,

Jizany,

Mahmood

et al. 2024

ISI

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Alzheimer's Disease (AD), which is left untreated, is the primary cause of neurodegenerative dementia, which primarily affects those over 65. Memory and thought processes are gradually disrupted by the irreversible brain disruption and, eventually, the capacity to fulfil simple tasks. Detecting AD early prevents its progression and diminishes its signs. The study aimed to build a classification model that might predict the early stages of Alzheimer's disease using accurate early-stage gene expression data from blood obtained from a clinical Alzheimer's dataset. The datasets used in this work were gathered from the Gene Expression Omnibus (GEO) and are GSE63060 and GSE63061.It has the right rows for (569 samples) and columns for (16382 genes). The suggested GWO gene selection method aims to identify the ideal feature subset for medical data. A model for predicting early Alzheimer's disease is proposed based on the modified Grey Wolf Optimizer and Support Vector Machine (GWO-SVM). These methods will help reduce the number of trivial and redundant genes in the original datasets. We attained an accuracy of 82%-88% using only the Support Vector Machine method. When we utilized several evolutionary algorithms to implement gene selection, we observed an increase in accuracy of 6%-11%, with modified Grey Wolf optimization with Crossover (CGWO) called the proposed algorithm (CGWO-SVM) achieving the highest accuracy of (97.41%) with the Alzheimer's disease dataset in comparison to the other competitive schemes in the existing literature. Thus, the proposed system is suitable for picking more accurate classification and interesting genes to increase classification accuracy while decreasing gene dimensions.

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Section: Introductionmentioning

confidence: 99%

Predicting Alzheimer's Disease Using a Modified Grey Wolf Optimizer and Support Vector Machine

Mohammed,

Jizany,

Mahmood

et al. 2024

ISI

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“…Early-stage dementia typically leads to erroneous diagnosis and, as a result, delays in receiving appropriate therapies. As a result, the development of useful and efficient biomarkers capable of establishing accurate correspondences and correlations with clinical symptoms has become a top priority [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Predicting Alzheimer's Disease Using Filter Feature Selection Method

2022

IJCCCE

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Alzheimer’s disease (AD) is caused by multiple variables. Alzheimer's disease development and progression are influenced by genetic variants. The molecular pathways causing Alzheimer's disease are still poorly understood. In Alzheimer's disease research, determining an effective and reliable diagnosis remains a major difficulty, particularly in the early stages (i.e., Moderate Cognitive Impairment (MCI)). Researchers and technologists working in the fields of machine learning and data mining can help improve the situation, early AD diagnosis but face a hurdle when it comes to high-dimensional data processing. By reducing irrelevant and redundant data from microarray gene expression data, the technique of feature selection can save computing time, improve learning accuracy, and encourage a deeper effect on the learning system or data. The feature selection strategy described in this article reduces data noise well. In particular, Pearson's correlation coefficient is used to assess data redundancy. The efficacy of these features is assessed using the Support Vector Machine (SVM) classification approach. The proposed approach has an accuracy of up to 91.1 %. As a result, newly established approaches for early diagnosis of Alzheimer's disease(AD) are being improved. Index Terms— Alzheimer’s Disease, Support vector machine, machine learning, feature selection, Pearson’s correlation coefficient.

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