Linear discriminant analysis and support vector machines for classifying breast cancer

Rustam, Zuherman; Amalia, Yasirly; Hartini, Sri; Saragih, Glori Stephani

doi:10.11591/ijai.v10.i1.pp253-256

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…In recent years, there is huge research attention towards the use of machine learning for medical applications due to the wide spreading cases of chronic kidney disease. Among the recent research conducted on the problem of chronic kidney disease prediction with machine learning algorithms, the most widely used algorithms of chronic kidney disease include the support vector machine (SVM) [7], and adaptive boosting algorithm [8].…”

Section: Related Workmentioning

confidence: 99%

Influence of Class Imbalance and Resampling on Classification Accuracy of Chronic Kidney Disease Detection

Salau¹,

Markus²,

Assegie³

et al. 2023

MMEP

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Chronic kidney disease is one of the leading causes of death around the world. Early detection of chronic kidney disease is crucial to the reduction of mortality caused as a result of the disease. Machine learning methods are recently becoming popular for the detection of chronic kidney disease. This study investigates the influence of resampling for chronic kidney disease detection using an imbalanced chronic kidney disease dataset. Choosing an optimal feature subset for medical datasets is important for improving the performance of data-driven predictive models. The influence of imbalanced class distribution on predictive models has become an increasingly important topic due to the recent advances in automatic decision-making processes and the continuous expansion in the volume of the data collected by medical institutions. To address the identified research gap, an experimental evaluation of synthetic minority oversampling and near miss undersampling technique was performed on a real-world chronic kidney disease dataset using several classification methods such as decision tree, random forest, K-nearest neighbor, adaptive boosting, and support vector machine. The results demonstrate that a number of variables, including performance metrics, classification algorithm, and dataset characteristics, influence the best class distribution.The study also offers useful information about resampling methods for an imbalanced classification problem which will help improve classification accuracy.

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Section: Related Workmentioning

confidence: 99%

Influence of Class Imbalance and Resampling on Classification Accuracy of Chronic Kidney Disease Detection

Salau¹,

Markus²,

Assegie³

et al. 2023

MMEP

View full text Add to dashboard Cite

show abstract

“…Sequential feature selection (SFS) method is wrapper feature selection [18], [19]. Sequential feature selection selects the last feature or the first feature in the dataset initially.…”

Section: Sequential Feature Selectionmentioning

confidence: 99%

Exploring the performance of feature selection method using breast cancer dataset

Assegie¹,

Tulasi²,

Kumar

2022

IJEECS

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Breast cancer is the most common type of cancer occurring mostly in females. In recent years, many researchers have devoted to automate diagnosis of breast cancer by developing different machine learning model. However, the quality and quantity of feature in breast cancer diagnostic dataset have significant effect on the accuracy and efficiency of predictive model. Feature selection is effective method for reducing the dimensionality and improving the accuracy of predictive model. The use of feature selection is to determine feature required for training model and to remove irrelevant and duplicate feature. Duplicate feature is a feature that is highly correlated to another feature. The objective of this study is to conduct experimental research on three different feature selection methods for breast cancer prediction. Sequential, embedded and chi-square feature selection are implemented using breast cancer diagnostic dataset. The study compares the performance of sequential embedded and chi-square feature selection on test set. The experimental result evidently shows that sequential feature selection outperforms as compared to chi-square (X2) statistics and embedded feature selection. Overall, sequential feature selection achieves better accuracy of 98.3% as compared to chi-square (X2) statistics and embedded feature selection.

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“…This method had high accuracy about 98.33%. In this study [82], both of linear discriminant analysis and SVM are compared by looking from accuracy, sensitivity, specificity, and F1-score. We will know which methods are better in classifying breast cancer dataset.…”

Section: Classificationmentioning

confidence: 99%

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Pour

Esmaeili

Romoozi

2022

IJECE

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Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study.

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Linear discriminant analysis and support vector machines for classifying breast cancer

Cited by 5 publications

References 22 publications

Influence of Class Imbalance and Resampling on Classification Accuracy of Chronic Kidney Disease Detection

Influence of Class Imbalance and Resampling on Classification Accuracy of Chronic Kidney Disease Detection

Exploring the performance of feature selection method using breast cancer dataset

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

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