Ovarian cancer classification using K-Nearest Neighbor and Support Vector Machine

Wibowo, Velery Virgina Putri; Rustam, Zuherman; Hartini, Sri; Maulidina, Faisa; Wirasati, Ilsya; Sadewo, Wismaji

doi:10.1088/1742-6596/1821/1/012007

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…k-Nearest Neighbors (k-NN) predict the class of data points based on feature similarity scores. k-NN has been used for ovarian cancer classification using different features, including gene expression data or clinical variables [12,13].…”

Section: Data Preprocessingmentioning

confidence: 99%

Explainable Artificial Intelligence based Ensemble Machine Learning for Ovarian Cancer Stratification using Electronic Health Records

Aelgani,

Vadlakonda

2023

IJRITCC

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The purpose of this study is to show how ensemble learning-driven machine learning algorithms outperform individual machine learning algorithms at predicting ovarian cancer on a biomarker dataset. Additionally, this study provides model explanations using explainable Artificial Intelligence methods, The method involved gathering and combining 49 risk factors from 349 patients. We hypothesize that ensemble machine learning systems are superior to individual Machine Learning systems in predicting ovarian cancer. The Machine Learning system consists of five individual Machine Learning and five ensemble Machine Learning systems were trained using K-10 cross validation protocols. These training models were then used to predict the development of benign ovarian tumors and ovarian cancer tumors patients. The AUC and Accuracy metrics for ensemble machine learning increased by 19% and 16%. The MCC and Kappa scores for ensemble Machine Learning also increased over individual machine learning by 29% and 33%, respectively. As a result, we draw the conclusion that ensembled-based algorithms outperform individual machine learning in terms of ovarian carcinoma prediction.

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Section: Data Preprocessingmentioning

confidence: 99%

Explainable Artificial Intelligence based Ensemble Machine Learning for Ovarian Cancer Stratification using Electronic Health Records

Aelgani,

Vadlakonda

2023

IJRITCC

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“…It was crucial to find many biomarkers to use in diagnostic tests that have high sensitivity and specificity. Wibowo, V. et al (2021) This research tested the performance of the supervised machine learning techniques of KNN and SVM with the RBF kernel on ovarian cancer classification data. In all, there were 203 and 5 characteristics taken from the Al Islam Bandung Hospital dataset.…”

Section: Ovarian Cancer Prediction Using Deep Learningmentioning

confidence: 99%

An Analysis For Early Diagnosis To Detect Ovarian Cancer

P. Thenmozhi, Dr. T. Parimalam

2023

tjjpt

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Ovarian cancer stands as the primary cause of mortality among gynecological cancers, creating an urgent need for innovative therapies. Despite the established correlation between the presence of tumor-infiltrating lymphocytes and improved prognosis, the integration of immunotherapy into the ovarian cancer treatment arsenal has not yet occurred. Over the past decades, extensive research has been dedicated to identifying early signs of ovarian cancer. Recognized as the "silent killer," ovarian cancer often remains asymptomatic in its early stages, leading to late-stage discoveries and poor prognosis. Early detection is pivotal for timely intervention based on symptomatic manifestations. Various categorization systems have been employed for ovarian tumor diagnosis, encompassing non-molecular factors such as tumor tissue, clinical stage, and pathological characteristics. These factors contribute to a comprehensive understanding of the intricate nature of cancer, guiding the development of effective therapeutic approaches. This review paper briefly outlines the preprocessing, feature extraction, and classification methodologies employed in the analysis of ovarian cancer across various studies from 2018 to 2023, encompassing several research papers.

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“…As [21] represents the comparison between the methods of machine learning for the classification of ovarian cancer. ML SVM and KNN with RBF (Radial basis function) kernelis used for this purpose.…”

Section: Figure 1 Medical Imaging With ML and Dlmentioning

confidence: 99%

Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges

Sajjad

Khan

Nawaz

et al. 2022

JCBI

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Machine and Deep learning has witnessed an exceptional amount of admiration in recent years. ML has ability to learn data itself by predicting uncertain conditions or future and classify categories with minimum intervention of human. While in DL, computers are able to automatically, learn useful features and representation precisely from raw data. ML and DL potentially a disruptive technology in predictive healthcare analysis. A detailed understanding and evaluation of the applications and principles of radiomics, machine and deep learning is an important task to construct possible solutions that are capable of accomplish compulsory and ethical requirements, which can enhance efficiency, quality and outcomes. Machine and deep learning extensively being use in medical image analysis, medical diagnostics and medical image technology. The wide scope and sudden progress of ML and DL has remarkably change the ways of diagnosis, prediction, classification and analyzing ovarian, lungs, brain, skin and various other types of cancer. In view of multiple applications of ML and DL, in this article a review conducted on ovarian cancer. This review comprises the detailed analysis of OC (prognosis, diagnosis, classification, evaluation) by covering all the major contributions of machine and deep learning. Furthermore, a literature taxonomy of the research conferred and emerging aspects analyzed. A section of discussion also signified to elaborate the limitations and future challenges of machine and deep learning comprises with ovarian cancer.

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Ovarian cancer classification using K-Nearest Neighbor and Support Vector Machine

Cited by 8 publications

References 20 publications

Explainable Artificial Intelligence based Ensemble Machine Learning for Ovarian Cancer Stratification using Electronic Health Records

Explainable Artificial Intelligence based Ensemble Machine Learning for Ovarian Cancer Stratification using Electronic Health Records

An Analysis For Early Diagnosis To Detect Ovarian Cancer

Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges

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