eprintid: 56782
rev_number: 10
eprint_status: archive
userid: 12460
dir: disk0/00/05/67/82
datestamp: 2023-03-01 07:44:43
lastmod: 2023-03-01 07:44:43
status_changed: 2023-03-01 07:44:43
type: thesis
metadata_visibility: show
contact_email: muh.khabib@uin-suka.ac.id
creators_name: Rebeccah Ndungi, NIM.: 21206051022
title: 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.
ispublished: pub
subjects: TB
divisions: S2_inf
full_text_status: restricted
keywords: Breast Cancer, Deep Learning, WHO,Health, Machine learning
note: Pembimbing: Ir. Maria Ulfah Siregar, S.Kom., MIT., Ph.D.
abstract: 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.
date: 2023-01-20
date_type: published
pages: 53
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: masters
thesis_name: other
citation:   Rebeccah Ndungi, NIM.: 21206051022  (2023) 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.  Masters thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/56782/1/21206051022_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/56782/2/21206051022_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf