Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

Sadewo, Wismaji; Rustam, Zuherman; Hamidah, Hamidah; Chusmarsyah, Alifah Roudhoh

doi:10.3390/sym12040667

Cited by 25 publications

(19 citation statements)

References 18 publications

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“…The proposed framework is evaluated by training a series of hierarchical classification models by considering several combinations of binary classifiers in all the three hierarchical steps, indicated in Figure 3 . The classification methods considered for individual binary classifiers include: (i) Gaussian Naïve Bayes (GNB) [40] , (ii) Decision Tree (DT) [41] , (iii) Support Vector Machine (SVM) [42] , (iv) k-Nearest Neighbors (kNN) [43] , (v) Random Forest Classifier (RFC) [41] , [43] and Logistic Regression (LR) [44] . In order to avoid overfitting, we have used \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k$ \end{document} -fold ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k = 5$ \end{document} ) cross validation technique as a resampling method for training and evaluating the performance of classification models.…”

Section: Resultsmentioning

confidence: 99%

Section: A Hard Hierarchical Decision Structurementioning

confidence: 99%

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Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure

Agarwal

Covarrubias‐Zambrano

Bossmann

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Objective: Pancreatic cancer (PC) is a silent killer, because its detection is difficult and to date no effective treatment has been developed. In the US, the current 5-year survival rate of 11%. Therefore, PC has to be detected as early as possible. Methods and procedures: In this work, we have combined the use of ultrasensitive nanobiosensors for protease/arginase detection with information fusion based hierarchical decision structure to detect PC at the localized stage by means of a simple Liquid Biopsy. The problem of early-stage detection of pancreatic cancer is modelled as a multi-class classification problem. We propose a Hard Hierarchical Decision Structure (HDS) along with appropriate feature engineering steps to improve the performance of conventional multi-class classification approaches. Further, a Soft Hierarchical Decision Structure (SDS) is developed to additionally provide confidences of predicted labels in the form of class probability values. These frameworks overcome the limitations of existing research studies that employ simple biostatistical tools and do not effectively exploit the information provided by ultrasensitive protease/arginase analyses. Results: The experimental results demonstrate that an overall mean classification accuracy of around 92% is obtained using the proposed approach, as opposed to 75% with conventional multi-class classification approaches. This illustrates that the proposed HDS framework outperforms traditional classification techniques for early-stage PC detection. Conclusion: Although this study is only based on 31 pancreatic cancer patients and a healthy control group of 48 human subjects, it has enabled combining Liquid Biopsies and Machine Learning methodologies to reach the goal of earliest PC detection. The provision of both decision labels (via HDS) as well as class probabilities (via SDS) helps clinicians identify instances where statistical model-based predictions lack confidence. This further aids in determining if more tests are required for better diagnosis. Such a strategy makes the output of our decision model more interpretable and can assist with the diagnostic procedure. Clinical impact: With further validation, the proposed framework can be employed as a decision support tool for the clinicians to help in detection of pancreatic cancer at early stages.

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Section: Resultsmentioning

confidence: 99%

Section: A Hard Hierarchical Decision Structurementioning

confidence: 99%

Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure

Agarwal

Covarrubias‐Zambrano

Bossmann

et al. 2022

IEEE J. Transl. Eng. Health Med.

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“…Kernel method is a method that uses kernel functions to operate algorithms in feature spaces that have higher dimensions. This method uses product operations between images of all image pairs in the feature space [18]. Accuracy for classifying objects in the right cluster is difficult to obtain in high dimensional data sets, measuring euclidean distances on k-means, c-means, or fuzzy c-medoids.…”

Section: Kernel Functionmentioning

confidence: 99%

Twin support vector machine using kernel function for colorectal cancer detection

et al. 2021

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Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artiﬁcial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.

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“…Jayadeva juga memperlihatkan bahwa dari segi waktu perhitungan dan keakurasian klasifikasi, T-SVM lebih unggul dibandingkan SVM standar. Selain itu, T-SVM juga diterapkan dalam berbagai masalah antara lain pengenalan suara, pembelajaran multi-label, klasifikasi penyakit Alzheimer, dan deteksi kanker pancreas [10][11][12] [13].…”

Section: Pendahuluanunclassified

Deteksi Penyalahgunaan Narkoba Dengan Metode Twin Bounded SVM

Tomasouw

Lesnussa

2021

BAREKENG: J. Il. Mat. & Ter.

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Twin Bounded SVM (TB-SVM) is an improvement of the Twin SVM method and has advantages in classification problems compared to standard SVM. In this research, linear TB-SVM and nonlinear TB-SVM methods will be applied to detect drug use based on 23 symptoms experienced. The training and testing data is divided into three partition data schemes (60/40 scheme, 70/30 scheme and 80/20 scheme) in order to determine the best level of accuracy that can be obtained. The test results show that the nonlinear TB-SVM with the RBF kernel has a better accuracy rate than the linear TB-SVM, that is 80% at 60/40 scheme, 90% at 70/30 scheme, and 95% at 80/20 scheme.

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Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

Cited by 25 publications

References 18 publications

Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure

Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure

Twin support vector machine using kernel function for colorectal cancer detection

Deteksi Penyalahgunaan Narkoba Dengan Metode Twin Bounded SVM

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