Classification Breast Cancer Revisited with Machine Learning

Parhusip, Hanna Arini; Susanto, Bambang; Linawati, Lilik; Trihandaru, Suryasatriya; Sardjono, Yohannes; Mugirahayu, Adella Septiana

doi:10.18517/ijods.1.1.42-50.2020

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…8 also demonstrates the strong interest in research in root exploit detection. Several detection terms, such as malware detection, intrusion detection system (IDS), machine learning, static analysis [22], [23] rootkit detection, and virtual machine introspection (VMI) were mentioned in the middle plot. VMI is a technique that externally monitors the runtime state of a system-level virtual machine.…”

Section: Words Analysismentioning

confidence: 99%

The Relevance of Bibliometric Analysis to Discover the Areaâ€™s Research Efforts: Root Exploit Evolution

Park

Ahmad²,

Ernawan³

et al. 2022

JOIV : Int. J. Inform. Visualization

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Malware steals, encrypts, and damages data of the targeted machines for private, money, or fame purposes. The types of malware are root exploit, cryptojacking, Trojan, worms, viruses, spyware, ransomware, and adware. Among these types, root exploit is one of the most destructive malware types since it disguises and obscures all types of malware and provides a mechanism for other malware to carry out malicious acts invisibly. In the interest to review the progress of root exploit efforts globally, there is a need to inspect all publications that involve root exploit. Among all malware reviews previously, to date, there is still no trace of any bibliometric analysis that demonstrates the research impacts of root exploit and trends in bibliometric analysis. Hence, this paper adopts bibliometric analysis specifically on root exploit studies which evaluate: (1) Wordcloud; (2) WordTreeMap; (3) Three fields plot; (4) Thematic evolution; (5) Thematic maps; (6) Correspondence analysis (CA); (7) Dendrogram; and (8) Multiple correspondence analysis (MCA). To conclude, our bibliometric discovers that; 1) Linux and Android become main interest in root exploit studies. 2) Types of root exploit in virtualization layer and studies to detect on this area are increasing. 3) USA and China have become the leaders in root exploit research. 4) Research studies are more towards memory forensics to detect root exploit, which is more promising. 5) Instead of researching new methods of root exploit in compromising victims, root exploit researchers were more focused on detecting root exploits.

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Section: Words Analysismentioning

confidence: 99%

The Relevance of Bibliometric Analysis to Discover the Areaâ€™s Research Efforts: Root Exploit Evolution

Park

Ahmad²,

Ernawan³

et al. 2022

JOIV : Int. J. Inform. Visualization

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show abstract

“…This study purposed to classified the selected features of GLCM based on breast density into four categories. This study proposes Random Forests (RF) [31], [33], Decision Tree [2], [19], [34], K-Nearest Neighbor [9] [35], and Multi SVM one-vs-one methods [36] because these methods produce a powerful performance in the studies that have been carried out. The classifiers use their default parameters to classify the mammograms.…”

Section: Rf-rfementioning

confidence: 99%

The Effect of Feature Selection on Gray Level Co-Occurrence Matrix (GLCM) for the Four Breast Cancer Classifications

Mawarni

Utaminingrum

Mahmudy

2022

JBBBE

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Breast cancer is ranked first as the most common cancer case affecting women in the world. Early detection of breast cancer can increase the chances of survival in patients. The role of the radiologist is necessary for the detection of breast cancer, and the radiologists often have limitations in conducting disease consultations with so many patients. The detection gives a subjective result because the process is based on the decision-making of the radiologists. In this work, we proposed a system to detect and classify breast cancer accurately to anticipate delays in patient handling and subjective result. We proposed a digital image processing method using mammograms to classify breast cancer into four categories based on tissue density, namely BI-RADS I, II, III, and IV. The main stages carried out in this research are images processing, feature extraction, data normalization, feature selection, classification, and parameter optimization. This method uses GLCM to extract texture features and two feature selection methods namely, RFE-RF and Chi-Square. The method was tested with various classifiers such as SVM, KNN, Random Forests, and Decision Trees. The hyper-parameters of the classifier were optimized using GridSearch. The final result is measure using accuracy. In this work, Random Forest with the RFE-RF gives the highest accuracy of 99.7%. Feature selection offers a significant impact on improving accuracy. The results of this work prove that our system can classify breast cancer with high accuracy. So that our system can solve problems to assist radiologists in screening mammograms and help make decisions to diagnose patients with breast cancer based on density.

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A Review of Machine Learning Algorithms on Different Breast Cancer Datasets

Sweetlin,

Saudia

2023

Lecture Notes in Electrical Engineering

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Classification Breast Cancer Revisited with Machine Learning

Cited by 4 publications

References 10 publications

The Relevance of Bibliometric Analysis to Discover the Areaâ€™s Research Efforts: Root Exploit Evolution

The Relevance of Bibliometric Analysis to Discover the Areaâ€™s Research Efforts: Root Exploit Evolution

The Effect of Feature Selection on Gray Level Co-Occurrence Matrix (GLCM) for the Four Breast Cancer Classifications

A Review of Machine Learning Algorithms on Different Breast Cancer Datasets

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