%0 Thesis
%9 Masters
%A Rebeccah Ndungi, NIM.: 21206051022
%B FAKULTAS SAINS DAN TEKNOLOGI
%D 2023
%F digilib:56782
%I UIN SUNAN KALIJAGA YOGYAKARTA
%K Breast Cancer, Deep Learning, WHO,Health, Machine learning
%P 53
%T MACHINE LEARNING ALGORITHM USING CLASSIFICATION AND REGRESSION TREES, LINEAR GAUSSIAN NAIVE BAYES, SUPPORT VECTOR MACHINES, CART AND K-NEAREST NEIGHBORS TO AID CANCER RESEARCH FIRMS IN DATA GROUPING AND BETTER ANALYSIS.
%U https://digilib.uin-suka.ac.id/id/eprint/56782/
%X World Health Organization publications disclose that breast cancer  is one of the most common diseases amongst the women, and that it  has a high death rate. Its prevalence is growing in developing nations,  where the vast majority of cases are discovered late in the disease’s  development. Preventing this malignancy may be accomplished with  safeguards and frequent examinations. Aside from that, early detection  of the illness may be beneficial in terms of battling the condition.  This deep learning algorithm is a strong approach for identifying features  about the breast mass that are often not visible to the human  eye through deep learning analysis of the mammogram. The algorithm  will then be evaluated through various metrics to validate on  its accuracy. Deep learning is a technology that will be used in this  project. The algorithm intends to help cancer firm research centres in  Kenya and globally in quicker grouping, identification and aid doctors  in quicker diagnosis and research.
%Z Pembimbing: Ir. Maria Ulfah Siregar, S.Kom., MIT., Ph.D.