Comparative Analysis of Dimension Reduction Methods

Çalişan, Mücahit; Talu, Muhammed Fatih

doi:10.46810/tdfd.707200

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…The main purpose of the size-reduction methods is to minimize the loss of data contained by the maximum reduction of data size. Choosing the most suitable data for the purpose is important in increasing the analysis's quality and obtaining meaningful results (19).…”

Section: Methodsmentioning

confidence: 99%

Assessment of COVID-19-Related Genes Through Associative Classification Techniques

ÇİÇEK

Kaya

Çolak

2022

Konuralp Tıp Dergisi

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Objective: This study aims to classify COVID-19 by applying the associative classification method on the gene data set consisting of open access COVID-19 negative and positive patients and revealing the disease relationship with these genes by identifying the genes that cause COVID-19. Methods: In the study, an associative classification model was applied to the gene data set of patients with and without open access COVID-19. In this open-access data set used, 15979 genes are belonging to 234 individuals. Out of 234 people, 141 (60.3%) were COVID-19 negative and 93 (39.7%) were COVID-19 positives. In this study, LASSO, one of the feature selection methods, was performed to choose the relevant predictors. The models' performance was evaluated with accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score.Results: According to the study findings, the performance metrics from the associative classification model were accuracy of 92.70%, balanced accuracy of 91.80%, the sensitivity of 87.10%, the specificity of 96.50%, the positive predictive value of 94.20%, the negative predictive value of 91.90%, and F1-score of 90.50%. Conclusions:The proposed associative classification model achieved very high performances in classifying COVID-19. The extracted association rules related to the genes can help diagnose and treat the disease.

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Section: Methodsmentioning

confidence: 99%

Assessment of COVID-19-Related Genes Through Associative Classification Techniques

ÇİÇEK

Kaya

Çolak

2022

Konuralp Tıp Dergisi

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Ovarian Cancer Prediction Using PCA, K-PCA, ICA and Random Forest

2021

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Ovarian cancer, which is the most common in women and occurs mostly in the post-menopausal period, develops with the uncontrolled proliferation of the cells in the ovaries and the formation of tumors. Early diagnosis is very difficult and in most cases, it is a type of cancer that is in advanced stages when first diagnosed. While it tends to be treated successfully in the early stages where it is confined to the ovary, it is more difficult to treat in the advanced stages and is often fatal. For this reason, it has been focused on studies that predict whether people have ovarian cancer. In our study, we designed a RF-based ovarian cancer prediction model using a data set consisting of 49 features including blood routine tests, general chemistry tests and tumor marker data of 349 real patients. Since the data set containing too many dimensions will increase the time and resources that need to be spent, we reduced the dimension of the data with PCA, K-PCA and ICA methods and examined its effect on the result and time saving. The best result was obtained with a score of 0.895 F1 by using the new smaller-sized data obtained by the PCA method, in which the dimension was reduced from 49 to 6, in the RF method, and the training of the model took 18.191 seconds. This result was both better as a success and more economical in terms of time spent during model training compared to the prediction made over larger data with 49 features, where no dimension reduction method was used. The study has shown that in predictions made with machine learning models over large-scale medical data, dimension reduction methods will provide advantages in terms of time and resources by improving the prediction results.

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Maki̇ne Öğrenmesi̇ Yöntemleri̇ İle Eği̇ti̇m Başarisina Etki̇ Eden Faktörleri̇n Modellenmesi̇

Bakan,

Kanbay

2024

İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi

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Sağlık, medya, bankacılık ve finans alanında sınıflandırma, kümeleme ve tahmin amacıyla kullanılan makine öğrenmesi günümüzde eğitim alanında da kullanılmaktadır. Bu çalışmada eğitim öğretim kurumlarının belirleyecekleri stratejilerde veya alacakları önlemlerde yol gösterici olması ve hatta daha büyük ana kütle, daha farklı okul türü ya da farklı kademelerde, farklı sektörlerde uygulanarak sonuçların genelleştirilmesine fayda sağlaması amacıyla makine öğrenmesi yöntemlerinden K-en yakın komşu, naive bayes, rastgele orman, destek vektör makineleri, karar ağaçları, boosting makine öğrenmesi sınıflandırma algoritmaları ile kurulan matematiksel modellemeler ile öğrencilerin akademik başarılarını etkileyen faktörler araştırılmıştır. Kurulan matematiksel modelin başarısına etki eden hiperparametreler ızgara taraması yöntemi ile belirlenerek maksimum model başarısı sağlanmıştır. Matematiksel modellemelerde akademik başarı ölçütü çıktı olarak belirlenerek; kurulan matematiksel modellerde çıktı ve girdi sayılarına ait model başarılarının değişimi incelenmiş; çıktıların ve girdilerin sayısının çeşitli yöntemlerle (denetimli ve denetimsiz yöntemlerle) azaltılması işlemlerinin matematiksel model başarısına etkileri gözlenmiştir. Sonuç olarak, en yüksek model başarılarının iki sınıf etiketli veri setine ait olduğu görülmüştür. K-en yakın komşu, naive bayes, rastgele orman, destek vektör makineleri, karar ağaçları, boosting model başarıları sırasıyla 0,62, 0,61, 0,96, 0,72, 0,86, 0,79 olarak elde edilmiştir.

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Comparative Analysis of Dimension Reduction Methods

Cited by 3 publications

References 11 publications

Assessment of COVID-19-Related Genes Through Associative Classification Techniques

Assessment of COVID-19-Related Genes Through Associative Classification Techniques

Ovarian Cancer Prediction Using PCA, K-PCA, ICA and Random Forest

Maki̇ne Öğrenmesi̇ Yöntemleri̇ İle Eği̇ti̇m Başarisina Etki̇ Eden Faktörleri̇n Modellenmesi̇

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