Feature Selection Method using Genetic Algorithm for Medical Dataset

Jothi, Neesha; Husain, Wahidah; Rashid, Nur’Aini Abdul; Syed-Mohamad, Sharifah Mashita

doi:10.18517/ijaseit.9.6.10226

Cited by 3 publications

(2 citation statements)

References 36 publications

(36 reference statements)

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“…The gene selection a technique was used on the dataset choose crucial genes for the prediction process in order to decrease the dimensionality of the gene space, to increase the prediction's precision [15]. The results of this step are selecting a subset of the most informative genes.…”

Section: Gene Selectionmentioning

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: Gene Selectionmentioning

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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show abstract

“…Jothi et al [9], a comparative study on machine learning algorithms namely, decision tree, random forest and multi-layer perception is conducted on the Wisconsin heart disease data repository. The algorithms are evaluated against their accuracy on heart disease prediction and the result shows that multi-layer perception, neural network is better on prediction of the heart disease.…”

Section: Related Workmentioning

confidence: 99%

Heart disease prediction model with k-nearest neighbor algorithm

Assegie¹

2021

IJ-ICT

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<span>In this study, the author proposed k-nearest neighbor (KNN) based heart disease prediction model. The author conducted an experiment to evaluate the performance of the proposed model. Moreover, the result of the experimental evaluation of the predictive performance of the proposed model is analyzed. To conduct the study, the author obtained heart disease data from Kaggle machine learning data repository. The dataset consists of 1025 observations of which 499 or 48.68% is heart disease negative and 526 or 51.32% is heart disease positive. Finally, the performance of KNN algorithm is analyzed on the test set. The result of performance analysis on the experimental results on the Kaggle heart disease data repository shows that the accuracy of the KNN is 91.99%</span>

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Feature Selection With Genetic Algorithms and Its Effect on Classification Performance in Medical Datasets

Deperlioğlu

2023

Mühendislik Bilimleri Ve Tasarım Dergisi

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Günümüzde çok büyük boyuttaki tıbbi veri tabanlarından, klinik karar destek sistemlerinin faydalı bilgiler elde etmesi oldukça zorlaşmıştır. Genetik algoritmalar (GA) yaygın olarak kullanılan bir özellik seçme yöntemidir ve en iyi çözümleri verebilir. Bu çalışmada, çok sayıda karmaşık verilere sahip olan tıbbi verilerden özellik seçimi yapmak ve en uygun özellik alt kümesini oluşturarak sınıflandırma başarısını artırmak için GA içeren bir model önerilmiştir. Önerilen yöntemin performansını değerlendirmek için çalışmada en çok bilinen ve rahatlıkla ulaşılabilen 5 tıbbi veri kümesi ve 7 farklı denetimli sınıflandırma yöntemi kullanılmıştır. Her veri kümesi ile her sınıflandırıcı için ayrı ayrı özellik seçimi ve sınıflandırma uygulamaları yapılmıştır. Bu uygulamalarda elde edilen sonuçlar, önerilen yaklaşımla yapılan sınıflandırmalarda, veri kümesine bağlı olarak, Doğruluk oranında dolayısıyla makine öğrenmesi modeli performansında ortalama %2 ile %21 arasında artış sağlandığını ortaya koymuştur. Ayrıca yapılan çalışmalarda denetimli sınıflandırma algoritmalarından Rastgele Ormanın bütün veri kümelerinde diğer algoritmalardan daha iyi sonuçlar verdiği görülmekte ve tıbbi veri kümelerindeki sınıflandırma başarısı ile öne çıktığı görülmüştür.

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Feature Selection Method using Genetic Algorithm for Medical Dataset

Cited by 3 publications

References 36 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

Heart disease prediction model with k-nearest neighbor algorithm

Feature Selection With Genetic Algorithms and Its Effect on Classification Performance in Medical Datasets

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