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Suanda, Aura Nisa Galuh
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Computer Science & IT

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CLASSIFY BREAST TUMORS INTO MALIGNANT (CANCEROUS) OR BENIGN (NON-CANCEROUS) WITH THE NAIVE BAYES METHOD Perwira, Yuda; Suanda, Aura Nisa Galuh; Lase, Yulianto
INFOKUM Vol. 10 No. 5 (2022): December, Computer and Communication
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v10i5.1125

Abstract

Breast Cancer is a deadly disease and a scourge for all women around the world, breast cancer occurs starting with the emergence of tumors that grow in the breast, but not all tumors that grow become malignant (cancer) sometimes tumors are also benign (non-cancerous) which are not dangerous and it is enough just to be lifted through surgery, for the need to know and classify the characteristics of tumors in the breast will become malignant (cancer) or benign (noncancer) so that people know based on the characteristics of the CT-Scan results of breast tumor data, then classify binaryly, namely cancer or non-cancer, classification techniques using the naïve bayes method which is proven to have high accuracy for classification in several studies, then testing the accuracy and prediction of breast tumors becoming malignant (cancerous) or benign (non cancerous) with the naïve bayes method, as for the results of this study it is known that from digital image data from needle aspirate the smoothness (FNA) of breast mass is known that the naïve bayes algorithm can classify tumor data that become benign and malignant well, and after testing the accuracy of performance and obtained accuracy from the naïve bayes method is 94.55%.
Co-Authors Lase, Yulianto Perwira, Yuda