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British Journal of Healthcare and Medical Research - Vol. 9, No. 3
Publication Date: June, 25, 2022
DOI:10.14738/jbemi.93.12535. Alseddiqi, M., AlMannaei, B., Najam, O., Atawi, K., & Al-Mofleh, A. (2022). The Prospective Benefits of Using Machine Learning for
the Prediction of Breast Cancer. British Journal of Healthcare and Medical Research, 9(3). 216-227.
Services for Science and Education – United Kingdom
The Prospective Benefits of Using Machine Learning for the
Prediction of Breast Cancer
Mohamed Alseddiqi
Clinical Engineering Directorate, king Hamad university hospital
Building -2435, Road 2835, Block 228 P.O. Box 24343
Busaiteen Kingdom of Bahrain
Budoor AlMannaei
Clinical Engineering Directorate, king Hamad university hospital
Building -2435, Road 2835, Block 228 P.O. Box 24343
Busaiteen Kingdom of Bahrain
Osama Najam
Clinical Engineering Directorate, king Hamad university hospital
Building -2435, Road 2835, Block 228 P.O. Box 24343
Busaiteen Kingdom of Bahrain
Khamis Atawi
Clinical Engineering Directorate, king Hamad university hospital
Building -2435, Road 2835, Block 228 P.O. Box 24343
Busaiteen Kingdom of Bahrain
Anwar AL-Mofleh
Clinical Engineering Directorate, king Hamad university hospital
Building -2435, Road 2835, Block 228 P.O. Box 24343
Busaiteen Kingdom of Bahrain
ABSTRACT
Improving the percentage of patients diagnosed with early-stage cancer is a vital
priority of the World Health Organization. Cancer is one of the most unsafe diseases
for humans, yet no enduring cure has been developed. Breast cancer is one of the
most common types of cancer in the Middle East region. Early diagnosis and
treatment of breast cancer can significantly improve the lives of millions of women
across the globe. Due to the advancement in technology, artificial intelligence and
machine learning have been used successfully to discover several dangerous
diseases, and serving in early analysis and treatment. Thus, the integration of
artificial intelligence and machine learning in the scientific field supports
enhancing morbity and mortality rates. This research is a systematic review on
breast cancer discovery and action using genetic sequencing or histopathological
imaging with the help of deep learning and machine learning.
Keywords: Breast cancer; breast cancer diagnosis; Artificial intelligence; Machine
Learning
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Alseddiqi, M., AlMannaei, B., Najam, O., Atawi, K., & Al-Mofleh, A. (2022). The Prospective Benefits of Using Machine Learning for the Prediction of
Breast Cancer. British Journal of Healthcare and Medical Research, 9(3). 216-227.
URL: http://dx.doi.org/10.14738/jbemi.93.12535
INTRODUCTION
Cancer is the main health difficulty in both developed and developing countries. The expected
number of new patients with cancer each year is predicted to increase from 10 million in 2000
to 16 million by 2020[1]. This amount continues to rise every year by 3-4%, and nearly 60% of
these cases will take place in developing countries where healthcare facilities and patient care
is limited. In the Eastern Mediterranean Region (EMR) cancer incidence is predicted to rise by
1.8-fold during the next decade [2]. Breast cancer (BC) is one of the most widespread
malignancies affecting women worldwide [3]. Around 5-10% of BC cases can possess breast
cancer susceptibility genes that influence to increased risk of malignancy [4-6]. In the Kingdom
of Bahrain there is a very high prevalence rate of BC as it accounts for 37.2% of all cancers in
females, and 20% of all new cancer diagnoses [7]. The age-standardized rate (ASR) per 100,000
women of 53.4 in 2010[8], which is the highest in all of Gulf Cooperation Council (GCC), and one
of the highest in the world [9, 10]. Figure 1(a, b) shows the number of new cases for all ages in
Bahrain [11, 12].
Figure 1a: Number of new cases in 2020, males all ages
Figure 1b: Number of new cases in 2020, females all ages
The incidence of BC correlates strongly with age, with the highest incidence rates observed in
older women and only 6% occurring in women under 40 years old. The mean age at diagnosis
in Bahrain was 50.7 years [13]. However, BC is usually more aggressive and advanced in the
younger age groups [14]. Unfortunately, despite recent advances in the management of BC the
ASR has decreased from 58.2 per 100,000 in 2000 to 53.4 per 100,000 in 2010, [15].
Furthermore, in Bahrain, the 5-year survival for BC was 63% compared to 80-90% in most
developed countries [16]. Survival in BC is thought to be dependent on many factors such as
the histology, tumor size, grade, lymph node status, hormone receptor status (HRS), estrogen
(ER), progesterone (PR) and human epidermal growth factor receptor (HER)-2 over- expression, as well as the stage at presentation.
Many types of breast cancer affect the human body; some of the most common breast cancers
are invasive ductal carcinoma and invasive lobular carcinoma. Both types of breast cancer grow
outside the ducts and lobular, and spread into other parts of the breast tissue. Invasive cancer
cells can also be metastasized to other organs and tissues of the body through blood vessels and
lymph vessels [17]. There are three types of tissues in women’s breasts: as shown in Figure2.
Fibrous tissue grips the breast tissue in place, Glandular tissue is the segment of the breast that
produces milk called the lobes, Epithelial tissue and fibrous tissue together are called fibro- glandular tissue. Fatty tissue fills the cavity between the fibrous tissue, lobes, and ducts. The
main functionality of fatty tissue is to determine the breast structure and size. Based on results,
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British Journal of Healthcare and Medical Research (BJHMR) Vol 9, Issue 3, June - 2022
Services for Science and Education – United Kingdom
the percentage of fibrous tissue and glandular tissue can determine the breast density collated
with the number of fatty tissues in a woman’s breast. Breast density is categorized into four
divisions based on mammography diagnostics:
1. The breasts are almost entirely fatty (about 10% of women).
2. There are scattered areas of fibro-glandular density (about 40% of women).
3. The breasts are evenly dense throughout (about 40% of women).
4. The breasts are extremely dense (about 10% of women).
Figure 2: The anatomy of the breast
Breast cancer diagnostics at an early stage can help to reduce mortality rates while increasing
survival rates. An important role in medical practice is to determine if a breast biopsy is
required through the utilization of imaging techniques such as mammography, ultrasound
imaging (US), magnetic resonance imaging (MRI), and Positron Emission Tomography (PET)
[18-20].
Due to obstacles faced by physicians during diagnosis, breast cancer detection from imaging
can be misinterpreted. Mammography, for example, can be compromised in detecting breast
cancer without the as it can cause many limitations in the diagnostic procedures, leading to
false clinical diagnostics. Enhancing the quality of imaging diagnostics of breast cancer by
applying principles and algorithms of artificial intelligence (AI) in mammography can improve
mammography detection quality and reduce physicians' workload by minimizing the second
call for diagnostic and improving waiting periods for the results.
Researchers found that AI models could predict breast cancer from scans with a similar level of
accuracy to expert physicians. Compared to human interpretation, AI showed an absolute
reduction in the error of cases where the cancer was incorrectly identified and cases where the
cancer was missed. [21]. Early diagnosis of breast cancer with AI gives the patients more
chances of enchased treatment and a greater rate of survival, even with the late-stage cancer
diagnosis. In specific, treatments can be modified and altered well to treat a patient through
their diagnosis. [22-23].
AI has become more popular in the last six years as it is improving rapidly in many fields; it is
noticeable that AI is ordinary in scientific and engineering communities [24]. There are many
implementations of AI that benefit the healthcare system. For example, “Drug development is