Classification of Human Skin Diseases using Data Mining

Kadhim, Qusay Kanaan

doi:10.22161/ijaers.4.1.25

Cited by 15 publications

(8 citation statements)

References 9 publications

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“…According to the ANOVA method, any gene having a weight more than the threshold has been neglected. Generally, ANOVA method uses the p-values to rank the relevant genes with small values in ascending order [16]. Consequently, the selected genes are used for further processing.…”

Section: Analysis Of Variance Methodsmentioning

confidence: 99%

Anomaly Detection in Human Disease: A Hybrid Approach Using GWO-SVM for Gene Selection

Mohammed,

Nazeeh,

Alisawi

et al. 2023

RIA

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The goal is to facilitate early disease detection. A Grey Wolf Optimizer (GWO) was implemented in the proposed method, a meta-heuristic algorithm known for its efficiency in reducing computational time for high-dimensional data. This optimization technique simplifies the problem by breaking it into manageable subsets. Following this, a filter approach, such as analysis of variance (ANOVA), was used to select informative genes from the reduced data. A Support Vector Machine (SVM) was also used as a classifier to select genes that efficiently categorize anomalous cases, serving as a fitness function-this combined approach, referred to as GWO-SVM, and aimed to reduce computational time while improving accuracy. The experimental results demonstrated that the proposed method achieved an accuracy rate of 96.46% in predicting disease detection, representing a significant improvement compared to previous methods. These findings underscore the potential of the GWO-SVM approach in advancing anomaly detection in human diseases.

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Section: Analysis Of Variance Methodsmentioning

confidence: 99%

Anomaly Detection in Human Disease: A Hybrid Approach Using GWO-SVM for Gene Selection

Mohammed,

Nazeeh,

Alisawi

et al. 2023

RIA

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“…In digital image processing, edge detection is one of the most popular techniques. Edges are focused on localized changes in the intensity of an image caused by the borders of object surfaces in a picture [30].…”

Section: Deep Learning Methodologymentioning

confidence: 99%

Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey

Ahmed

Kadhem

2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract"><strong>—</strong> Chest imaging diagnostics is crucial in the medical area due to many serious lung diseases like cancers and nodules and particularly with the current pandemic of Covid-19. Machine learning approaches yield prominent results toward the task of diagnosis. Recently, deep learning methods are utilized and recommended by many studies in this domain. The research aims to critically examine the newest lung disease detection procedures using deep learning algorithms that use X-ray and CT scan datasets. Here, the most recent studies in this area (2015-2021) have been reviewed and summarized to provide an overview of the most appropriate methods that should be used or developed in future works, what limitations should be considered, and at what level these techniques help physicians in identifying the disease with better accuracy. The lack of various standard datasets, the huge training set, the high dimensionality of data, and the independence of features have been the main limitations based on the literature. However, different architectures of deep learning are used by many researchers but, Convolutional Neural Networks (CNN) are still state-of-art techniques in dealing with image datasets.</p>

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“…Differences between entropy and conditional entropy can be used to assess how important a gene is in a certain category [16]. The ability of the predictor variable to categorize the dependent variable is determined by the IG, a kind of filter-based gene selection [17]. The information theory-based IG technique determines the statistical dependence between two variables [18].…”

Section: Information Gain (Ig)mentioning

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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Classification of Human Skin Diseases using Data Mining

Abstract: Abstract-

Cited by 15 publications

References 9 publications

Anomaly Detection in Human Disease: A Hybrid Approach Using GWO-SVM for Gene Selection

Anomaly Detection in Human Disease: A Hybrid Approach Using GWO-SVM for Gene Selection

Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey

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

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