IMPLEMENTATION OF NAIVE BAYES METHOD IN CLASSIFICATION OF BREAST CANCER DISEASE

Alamsyah, Alamsyah and Prasetyo, Eko and Adityo, R Dimas (2017) IMPLEMENTATION OF NAIVE BAYES METHOD IN CLASSIFICATION OF BREAST CANCER DISEASE. Journal of Electrical Engineering and Computer Sciences, 2 (1). pp. 191-194. ISSN P.ISSN: 2528-0260 E-ISSN: 2579-5392

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Abstract

Less knowledge of early symptoms of breast cancer and how to deal with it early and the number of specialist doctors who are still limited is one factor contributors because of the increasing number of people affected by breast cancer disease. The development of breast cancer disease classification system aims to predict the early diagnosis of breast cancer disease in users or patients into two categories of malignant or benign. The initial diagnoses of this system prediction variable include Clump Thickness, Uniformity of Cell Size, Uniformity of Cell Shape, Marginal Adhesion, Single Epithelial Cell Size (Single Epithelial Cell) Size), Bare nuclei, Bland Chromatin, Normal nucleoli, Mitosis Using the naive bayes method to process diagnostic data in patients, the results of this system test show that the system is able to predict and classify breast cancer disease into two categories (malignant or benign) with the amount of data testing of 500 data. With the output of malignant or YA and benign or NO, the system is able to predict with an accuracy value of 98%.Less knowledge of early symptoms of breast cancer and how to deal with it early and the number of specialist doctors who are still limited is one factor contributors because of the increasing number of people affected by breast cancer disease. The development of breast cancer disease classification system aims to predict the early diagnosis of breast cancer disease in users or patients into two categories of malignant or benign. The initial diagnoses of this system prediction variable include Clump Thickness, Uniformity of Cell Size, Uniformity of Cell Shape, Marginal Adhesion, Single Epithelial Cell Size (Single Epithelial Cell) Size), Bare nuclei, Bland Chromatin, Normal nucleoli, Mitosis Using the naive bayes method to process diagnostic data in patients, the results of this system test show that the system is able to predict and classify breast cancer disease into two categories (malignant or benign) with the amount of data testing of 500 data. With the output of malignant or YA and benign or NO, the system is able to predict with an accuracy value of 98%.

Item Type:	Article
Uncontrolled Keywords:	Classification, Naive Bayes, Breast Cancer.
Subjects:	Technology
Divisions:	Faculty of Engineering
Depositing User:	Perpus Ubhara Surabaya
Date Deposited:	19 Jun 2024 06:01
Last Modified:	19 Jun 2024 06:01
URI:	http://eprints.ubhara.ac.id/id/eprint/2538

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