Ovarian Cancer Detection and Classification Using Machine Leaning

Aditya, M G; Amrita, I.; Kodipalli, Ashwini; Martis, Roshan Joy

doi:10.1109/iceeccot52851.2021.9707954

Cited by 18 publications

(2 citation statements)

References 17 publications

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“…The most successful outcome was achieved using the random forest (RF) algorithm, which attained an accuracy rate of approximately 72%. Also, the RF algorithm was proven the best classifier in [19], with the obtained accuracy of 90.5% using the median imputation. In [20], the SVM model was employed in conjunction with K-Nearest Neighbor (KNN) model to predict OC, where the use of SVM achieved a better accuracy rate than the KNN, which was about 97.16%.…”

Section: Related Workmentioning

confidence: 96%

Ovarian Cancer Detection Based on Elman Recurrent Neural Network

Farhan,

Kod,

Taqi

et al. 2024

Period. Polytech. Elec. Eng. Comp. Sci.

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The early detection of cancers increases the possibility of health recovery and prevents the disease from becoming a silent killer. This study introduces an effective method for identifying ovarian cancer (OC) using Elman Recurrent Neural Network (ERNN), which can recognize cancer via mass spectrometry data. The network has a topology of 100 input neurons for receiving data, five neurons for hidden and context layers, and two output nodes to indicate the status. The proposed method uses reduced-size features, including ion concentration levels at specific mass/charge values, which are trained using various learning algorithms to determine the suitable one that achieves the best results. The experimental results show that all the training algorithms achieve about 100% performance rate, with the Levenberg Marquardt (LM) being the most accurate and fastest algorithm, which converges after six epochs and achieves 0.0035, 0.0045 and 0.0045 mean square errors for training, validation, and test performances, respectively. Based on comparative results, the proposed LM-ERNN method outperforms other OC detection methods and holds promise for detecting other types of cancer.

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Section: Related Workmentioning

confidence: 96%

Ovarian Cancer Detection Based on Elman Recurrent Neural Network

Farhan,

Kod,

Taqi

et al. 2024

Period. Polytech. Elec. Eng. Comp. Sci.

View full text Add to dashboard Cite

show abstract

“…AI modeling has also been used to create various machine and deep learning algorithms to predict the type and different stages of OC using available clinical data such as blood tests, imaging data, patients' family history, and background. 182,183 Such AI modeling can provide a logical decision-making framework by identifying variables that predict the OC stage to choose an effective personalized treatment strategy. Kawakami et al 184 showed the capability of machine learning models as a prediction system for epithelial ovarian cancer (EOC) diagnostic and prognostic by using seven different supervised machine learning classifiers to drive diagnostic and prognostic information from 32 parameters of available clinical data including blood tests of patients.…”

Section: Sensorsmentioning

confidence: 99%