Incorporating EBO-HSIC with SVM for Gene Selection Associated with Cervical Cancer Classification

Geeitha, S.; Thangamani, M.

doi:10.1007/s10916-018-1092-5

Cited by 16 publications

(2 citation statements)

References 37 publications

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“…In addition, Jung-Hoon et al proposed a new machine learning method for early detection of hepatocellular carcinoma to help doctors solve clinical problems by combining genetic algorithms, support vector machines and feature optimization [ 32 ]. Geeitha et al and Thangamani et al proposed a support vector machine risk scoring system for ovarian cancer patients [ 33 , 34 ]. By selecting miRNA sets, support vector machine (SVM) classifiers were constructed to analyze miRNA and clinical factors independently related to prediction, and a risk scoring system was constructed.…”

Section: Discussionmentioning

confidence: 99%

An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries

Zhang

Chen

et al. 2020

Computational and Mathematical Methods in Medicine

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Most developing countries face huge challenges in the medical field; scarce medical resources and inadequate medical personnel will affect the development and stability of the society. Therefore, for most developing countries, the development of intelligent medical systems can greatly alleviate the social contradictions arising from this problem. In this study, a new data decision-making intelligent system for prostate cancer based on perceptron neural network is proposed, which mainly makes decisions by associating some relevant disease indicators and combining them with medical images. Through data collection, analysis and integration of medical data, as well as the disease detection and decision-making process, patients are given an auxiliary diagnosis and treatment, so as to solve the problems and social contradictions faced by most developing countries. Through the study of hospitalization information of more than 8,000 prostate patients in three hospitals, about 2,156,528 data items were collected and compiled for experiment purposes. Experimental data shows that when the patient base increases from 200 to 8,000, the accuracy of the machine-assisted diagnostic system will increase from 61% to 87%, and the doctor’s diagnosis rate will be reduced to 81%. From the study, it is concluded that when the patient base reaches a certain number, the diagnostic accuracy of the machine-assisted diagnosis system will exceed the doctor’s expertise. Therefore, intelligent systems can help doctors and medical experts treat patients more effectively.

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Section: Discussionmentioning

confidence: 99%

An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries

Zhang

Chen

et al. 2020

Computational and Mathematical Methods in Medicine

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“…SVM was applied in many fields, such as economics [6], electrics [7], and medical science [8]. Especially in the field of cancer diagnosis, many studies have already proven the excellent performance of SVM classifier [9][10][11]. SVM uses the principle of structural risk minimization instead of empirical minimization and it can obtain a better generalization ability from limited samples.…”

Section: Introductionmentioning

confidence: 99%

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

2019

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As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.

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An in-depth and contrasting survey of meta-heuristic approaches with classical feature selection techniques specific to cervical cancer

Kurman

Kisan

2023

Knowl Inf Syst

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Incorporating EBO-HSIC with SVM for Gene Selection Associated with Cervical Cancer Classification

Cited by 16 publications

References 37 publications

An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries

An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

An in-depth and contrasting survey of meta-heuristic approaches with classical feature selection techniques specific to cervical cancer

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