Prediction of breast cancer based on machine learning

Background: The existence of Higher Education has three main tasks, namely carrying out education and teaching, research, and community service. These three things constitute the Tridharma of Higher Education. In Indonesia, breast cancer is the second most common cancer suffered by women after oral/cervical cancer. Breast cancer generally attacks women who are over 40 years old. However, young women can get this cancer. To find early symptoms of breast cancer can be detected early by women themselves. Women can routinely perform the Breast Self-Examination (BSE) method by massaging and feeling around their breasts to find out whether or not there are lumps around the breasts. BSE is an effective way to detect breast cancer as early as possible.
 Objective: Carry out community service by conducting activities to teach breast self-examination (BSE) to young women.
 Method: This community service activity consists of counseling related to information on early detection of breast cancer and followed by a demonstration of breast self-examination (BSE) using the Breast Phantom tool.
 Result: Counseling and early detection of breast cancer was attended by 45 students of Madrasah Aliyah. Most students do not know about early detection of breast cancer and how to check BSE. After being given information about BSE, adolescents felt satisfied and committed to be able to carry out early detection of breast cancer.
 Conclusion: Community service activities have been carried out regarding information on early detection of breast cancer in young women.

Background Earlier detection of breast cancer through mammography screening has reduced disease-specific mortality; however, confounding issues such as technical challenges, breast density, and tumor size can result in false negatives and ultimately later stage diagnosis. Next generation liquid biopsy has the potential to complement mammography and enable earlier detection for more women. We have previously demonstrated high sensitivity and specificity for early detection of invasive breast cancer (IBC) by utilizing a novel category of cancer-associated small RNAs, termed orphan noncoding RNAs (oncRNAs), through a liquid biopsy platform. Here, we further improve the ability to detect breast cancer in a larger, multi-source cohort through an AI-driven approach and demonstrate potential for detection of ductal carcinoma in-situ (DCIS). Methods We utilized The Cancer Genome Atlas (TCGA) small RNA-seq database to discover a library of 20,538 oncRNAs, through a female-specific analysis, that were significantly enriched among 1,103 breast tumors compared to 349 normal tissue samples spanning multiple tissue sites. The diagnostic performance of these oncRNAs were assessed in an independent cohort of archived serum samples from 663 female individuals, sourced from Indivumed (Hamburg, Germany), Proteogenex (Inglewood, CA), and MT Group (Los Angeles, CA), including 279 breast cancer patients of various stages (221 IBC and 58 DCIS; mean age: 57.0 ± 13.8 years; ever-smoker: 25.8%) and 304 age-matched controls (mean age: 58.5 ± 13.9 years; ever-smoker: 23.4%) without breast cancer. All samples were collected between 2010–2022 at time of diagnosis for breast cancer patients. We sequenced the small RNA content of these samples at an average depth of 25.28 ± 9.37 million 50-bp single-end reads. We detected 18,025 (87.8%) unique breast cancer-specific oncRNA species within at least one sample from the study cohort. We then trained a generative AI model using 5-fold cross-validation to predict cancer status for all samples. Results Our oncRNA-based model achieved an overall AUC of 0.95 (95% CI, 0.93–0.97) for prediction of IBC versus cancer-free controls with a sensitivity of 0.87 (0.82–0.91) at 90% specificity. We observed high sensitivities, also at 90% specificity, across all tumor stages and tumor sizes (Table 1). Sensitivities for the earliest stage and smallest tumor size were 0.87 (0.78–0.93) and 0.81 (0.61–0.93) for Stage I (n=83) and T1a–b ( >1mm to ≤10mm; n=26), respectively. Additionally, in a small single-source cohort, we also saw high model accuracy and sensitivity for DCIS, which we aim to confirm in additional cohorts. While our overall cancer cohort primarily consisted of individuals with luminal breast cancer, our model had high sensitivities across all breast cancer subtypes at 0.90 (0.84–0.94), 0.73 (0.59–0.85), and 0.86 (0.42–1.0) for luminal (n=181), HER2 positive (n=49), and triple negative (n=7), respectively. Conclusions We further demonstrate the potential utility of oncRNAs as a blood-based biomarker using an AI algorithm for sensitive and accurate early detection of breast cancer in a large cohort. Additionally, we have shown that this oncRNA-based assay performs well in detecting small, early-stage invasive breast tumors, with potential to detect precursors of breast cancer. Table 1: Model sensitivity in breast cancer by tumor stage and size For each tumor stage and size, as defined by the AJCC 7th Edition breast cancer staging system, sensitivity and 95% Pearson-Clopper confidence intervals (CI) are reported at 90% specificity for the number of samples (N). Citation Format: Noura Tbeileh, Taylor Cavazos, Mehran Karimzadeh, Jeffrey Wang, Alice Huang, Dung Ngoc Lam, Seda Kilinc, Jieyang Wang, Xuan Zhao, Andy Pohl, Helen Li, Lisa Fish, Kimberly Chau, Marra Francis, Lee Schwartzberg, Patrick Arensdorf, Hani Goodarzi, Fereydoun Hormozdiari, Babak Alipanahi. Cell-free orphan noncoding RNAs and AI enable early detection of invasive breast cancer and ductal carcinoma in-situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-13-08.

Introduction: The use of social media by healthcare professionals to create disease awareness has been increased worldwide. It is easily available and accessible to the larger number of populations and hence helps in bridging the gap between doctor and patient. Breast cancer is the leading cause of cancer among women in India and the main reason for higher mortality among them is the late presentation to the surgeons, owing to various myths and social and physical taboos. We conducted a study to assess the awareness of breast cancer among the women in general population and also to check the effectiveness of WhatsApp in improving women's knowledge regarding the risk reduction, myths, early detection and treatment of breast cancer. The study was a pre-post health educational study including 146 wom Methods: en. Multiple WhatsApp groups were created to share information regarding breast cancer awareness, early detection, risk factors, common myths, self-breast examination and treatment of breast cancer in the form of audio, video, text and images. The group was active for 4-5 days as an interactive session. Data were collected through google forms. We noticed a signicant improvement in the knowledge of women regard Results: ing the common myths regarding breast cancer and the symptoms of breast cancer. There was signicant increase in performance of self-breast examination from 14.4% to 84.6% (p- <0.001). Most of the women found the video of self-breast examination useful. The commonly Conclusion: used social networking site like WhatsApp can be used as a tool to assess the awareness of breast cancer among women. This also helped in exchange of the knowledge among women, which in turn helped in the risk reduction and early detection of breast cancer. This app is a useful tool that can be incorporated into health education strategies that focus on breast cancer.

Early detection and diagnosis of breast cancer are critical for saving lives. This paper addresses two major challenges associated with this task: the vast amount of data processing involved and the need for early detection of breast cancer. To tackle these issues, we developed thirty hybrid architectures by combining five deep learning techniques (Xception, Inception-V3, ResNet50, VGG16, VGG19) as feature extractors and six classifiers (random forest, logistic regression, naive Bayes, gradient-boosted tree, decision tree, and support vector machine) implemented on the Spark framework. We evaluated the performance of these architectures using four classification criteria. The results, analyzed using Scott Knott's statistical test, demonstrated the effectiveness of merging deep learning feature extraction techniques with traditional classifiers for classifying breast cancer into malignant and benign tumors. Notably, the hybrid architecture using logistic regression as the classifier and ResNet50 for feature extraction (RESLR) emerged as the top performer. It achieved impressive accuracy scores of 98.20%, 96.59%, 96.64%, and 94.84% across the Break-His dataset at different magnifications (40X, 100X, 200X, and 400X) respectively. Additionally, RESLR achieved an accuracy of 97.05% on the ICIAR dataset and a remarkable accuracy of 95.31% on the FNAC dataset.

Early Detection of Breast Cancer in Women

Healthcare systems around the world are facing huge challenges in responding to trends of the rise of chronic diseases. Early detection of breast cancer is essential for successful treatment since it is a common and potentially fatal condition. Based on clinical data, machine learning algorithms have shown potential in the categorization of breast cancer. This work aimed to build classification models Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Random Forest (RF), Logistic Regression (LR), and Artificial Neural Network (ANN) for Diagnostic Wisconsin Breast Cancer Dataset (WDBC) also improves classifier performance by using feature selection optimization and cross validation. The Particle Swarm Optimization (PSO) technique is used to select relevant, irredundant and most informative features that dependent on the performance of a classifier utilizing certain characteristics. The effectiveness of five distinct classifiers is assessed in this work. According to the findings, PSO-based SVM classifier has the greatest mean subset accuracy over a wide variety of training testing ratios.

Background: Our 2004 survey of breast cancer survivors in the Katowice region (Poland) showed that the detection of the disease was triggered by self-examination in 58.9%, mammography in 19.2%, and clinical examination in 19.7% of cases. The purpose of the current study (2019/2020) was to determine if the implementation of national screening (mammography) in 2007 resulted in an increase of the relative contribution of mammography to detection of cancer. Methods: Subjects were 215 breast cancer patients, members of self-support groups in Katowice region. The questionnaire included questions on early detection of breast cancer, participation in screening, and socio-economic status. Results: Early detection of cancer was initiated by self-examination in 63.7%, mammography in 22.8%, clinical examination in 13.5% of subjects. Age at detection depended on the method (p < 0.001): 54.1 ± 10.8 years for self-examination, 60.0 ± 7.6 years for mammography, and 58.7 ± 10.8 years for clinical examination. Conclusions: Both in 2004 and 2020 self-examination is the most frequent method of early detection of breast cancer in the study area. The contribution of mammography remains on a low level (23%). This finding could be explained by a low participation in screening and by age of 50 years used as the entry criterion to national screening of breast cancer in Poland.

Fibroadenoma is the most benign breast neoplasm. These tumors grow in young women with the highest frequency in young women 21-25 years, with a prevalence of more than 9% of the female population affected by fibroadenoma. Breast cancer still has a high likelihood of being cured when it comes early or early. Early detection of breast cancer is an examination program to recognize breast cancer while still small, and before the cancer has a chance to spread. One of the early detection efforts is clinical breast examination (SADANIS) which is carried out at least once every 3 years or if abnormalities are found in the process of self breast examination (BSE) and carried out by trained health workers. The purpose of the study was to determine the relationship between knowledge and early detection of breast cancer risk in young women through SADANIS examination. Observational research method with cross sectional approach. The sample is a student of SMK 1 Palu and a student of AKBID Cendrawasih Palu. The results showed that there was a significant relationship between knowledge about SADANIS examination and the practice of SADANIS examination in young women. In line with the Wantini and Indrayani (2018) study that there is a meaningful relationship between knowledge with early detection of breast cancer through SADANIS examination based ontest Fisher Exact's with a p-value of 0.012&lt;α (0.05), so there is a strong relationship between young women's knowledge BSE in early detection of breast cancer as evidenced by the p-value 0.007&lt;α (0.05). The conclusion is that the knowledge of young women about sadanis examination is still low Keywords: Knowledge, Early Detection of Breast Cancer, Adolescent Girls, SADANIS

Currently, as governments across the world attempt to halt the spread of COVID-19, many of their efforts are being hampered by shortages of crucial testing kits. Even in many developed countries, the health system has come to the point of standstill due to the increasing demand for intensive care units. One study reveals that 40,000 women die in a year from breast cancer, which is one woman every 13 minutes dying from this disease every day. Breast cancer remains the number one form of cancer that women are diagnosed with around the world. Any hospital in the world will have a multidisciplinary team of experts including radiologists, oncologists, surgeons, and other respiratory physicians and consultants. This multidisciplinary team uses a wide variety of data taken from CT scans, x-rays, PET scans, biopsies, clinical examination, imaging mammography, ultrasound (sonography), pathology biopsy, and fine-needle aspiration to identify whether a patient has cancer. Individuals with cancer, particularly those who are receiving systemic anticancer treatments, have been postulated to be at increased risk of mortality from COVID-19. Therefore, it is the need of an hour to implement an early detection of cancer that will prevent patients from COVID-19 and due treatment will be provided. Breast cancer that is detected early is far easier to cure, but at the same time it is difficult to detect. An accurate breast cancer staging is an essential task performed by pathologists (experts who examine the laboratory samples) to inform clinical management for the appropriate treatment. For instance, evaluation of the extent of cancer spread by histopathologic analysis or microscopic examination is an important part of breast cancer staging, and the process involves the decision-making of human behavior with time-consuming and costly sample examination and a period of more than one month to have breast cancer type, stage, etc., to decide the appropriate therapy to patients by the time it can affect the results of therapeutic treatments and survival of patients. The application of artificial intelligence (AI) machine learning technology with deep learning algorithms to whole-slide pathology images can potentially improve the diagnostic accuracy of breast cancer at a very early stage. I have developed a simple machine-learning model using the Python programming language and neural network, inspired by the structure of the human brain, with 12 attributes, out of which 10 real-valued features from each cell nucleus obtained from the Wisconsin diagnostic breast cancer dataset available in public domain for the education capacity that explains about the stage of breast cancer M (Malignant) and B (Benign). This model predicts the presence of breast cancer with an accuracy of 98.8% at a very early stage. The continuous development of technology in the medical field will save countless lives and the overall quality of human life will continue to improve over time. Citation Format: Subash Kumar. Early detection of breast cancer during the Covid-19 pandemic using artificial intelligence neural networks [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-003.

Background and methodsThe prevention and control of breast cancer in sub-Saharan Africa (SSA) is an increasingly critical public health issue. Breast cancer is the most frequent female cancer in SSA and mortality rates from this disease are the highest globally. Breast cancer has traditionally been considered a disease of high-income countries, and programs for early detection have been developed and implemented in these settings. However, screening programs for breast cancer in SSA have been less effective than in high-income countries. This article reviews the literature on breast cancer in SSA, focusing on early detection practices. It then examines the case for and against mammography and other early detection approaches for breast cancer in SSA.ResultsWomen with breast cancer in SSA are younger compared with high-income countries. Most women present with advanced disease and because treatment options are limited, have poor prognoses. Delay between symptom onset and healthcare seeking is common. Engagement with early detection practices such as mammography and breast examination is low and contributes to late stage at diagnosis.DiscussionWhile early detection of breast cancer through screening has contributed to important reductions in mortality in many high-income countries, most countries in SSA have not been able to implement and sustain screening programs due to financial, logistical and sociocultural constraints. Mammography is widely used in high-income countries but has several limitations in SSA and is likely to have a higher harm-to-benefit ratio. Breast self-examination and clinical breast examination are alternative early detection methods which are more widely used by women in SSA compared with mammography, and are less resource intensive. An alternative approach to breast cancer screening programs for SSA is clinical downstaging, where the focus is on detecting breast cancer earlier in symptomatic women. Evidence demonstrates effectiveness of clinical downstaging among women presenting with late stage disease.ConclusionsApproaches for early detection of breast cancer in SSA need to be context-specific. While screening programs with mammography have been effective in high-income countries, evidence suggests that other strategies might be equally important in reducing mortality from breast cancer, particularly in low-resource settings. There is a strong argument for further research into the feasability and acceptability of clinical downstaging for the control of breast cancer in SSA.

Tumor antigens, expressed early in disease, which can induce specific antibody response could serve as biomarkers for early detection of human breast cancer. However, the ability to obtain sera from women prior to the development of breast cancer is limited, which makes it difficult to identify early antigens directly from breast cancer patients. Transgenic mouse models have been proved to demonstrate similar antigen repertoire to breast cancer patients. Therefore, we propose to identify early tumor antigens in neu transgenic mice and use them as diagnostic targets for early detection of human breast cancer. We have collected serial sera from neu transgenic mice, sera collected before the animals develop palpable tumor “pre-diagnostic sera”. With serological analysis of recombinant cDNA expression libraries (SEREX) approach, we were able to identify nine tumor antigens from pre-diagnostic sera, Rpl5, TNFaip3/A20, Pdhx, Otud6b, Stk39, Zfp238, Dnajc10, Lgals8 and Vps35. These antigens demonstrated detectable IgG antibody response prior to tumor development in neu transgenic mice. IgM is the first antibody to be produced during a humoral immune response, so we questioned whether including antigen specific IgM with IgG responses could achieve a better diagnostic performance. Firstly, we analyzed receiver-operating-characteristic (ROC) curves of these antigens by IgG and IgM ELISA analysis in neu transgenic mice and 26 FVB control mice. By combining IgG and IgM ELISA, we were able to achieve a higher AUC: Otud6b and Stk39 show an AUC of 0.882, and adding Lgals8 IgG ELISA to this panel can achieve an AUC of 0.924 in discriminating cases from control mice. We documented that these autoantibodies are also detectable in the serum of women with breast cancer and then screened a panel of sera derived from the Women's Health Initiative cohort (samples obtained 6-18 months prior to the development of breast cancer) and case matched controls. We evaluated serum for the presence of IgG and IgM specific autoantibodies to Pdhx, Otud6b, and Stk39. With both IgG and IgM ELISA analysis, a panel combining the three antigens demonstrates an AUC of 0.633 in patients diagnosed within 5 month and an AUC of 0.688 in patients diagnosed within 5-18 month of the blood draw. In summary, we conclude that tumor antigens identified in neu transgenic mouse model can be used for early detection and diagnosis of breast cancer in women. The combination of antigen specific IgG and IgM antibodies may enhance the sensitivity of the assay for early detection. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2683. doi:10.1158/1538-7445.AM2011-2683

The present study was conducted to assess the awareness and practice regarding early detection and prevention of breast and cervical cancers among women of kudumbashree units. A quantitative approach and non experimental descriptive design was used for the study. The study was theoretically supported by Nola J Pender's revised health promotion model. A total of 504 women of kudumbashree units were selected using multistage sampling technique. The data was collected using socio personal data sheet, structured questionnaire to measure the awareness and a rating scale to rate the practice regarding early detection and prevention of breast and cervical cancers, all developed by the researcher. The data was analysed using descriptive and inferential statistics. The findings of the study revealed that 81.9% of women of kudumbashree units had average awareness and 59.5% of women of kudumbashree units had good practice. There was no significant correlation between awareness and practice among women of kudumbashree units regarding early detection and prevention of breast and cervical cancers. The selected variables namely education of women of kudumbashree units, occupation of women of kudumbashree units and previous exposure to information regarding breast or cervical cancers had association with awareness regarding early detection and prevention of breast and cervical cancers. Also, there is no association between practice regarding early detection and prevention of breast and cervical cancers and any of the selected variables. An information booklet was prepared on early detection and prevention of breast and cervical cancers and distributed to all study participants.

BSE (Breast Self-Examination) behavior is an effort to detect early or prevent breast cancer symptoms. One of the causes of the high incidence of breast cancer is the lack of education about breast cancer among the younger generation in early detection of breast cancer. The purpose of this study was to determine whether there is an effect of health education using phantom media on knowledge and behavior about breast self-examination (BSE) as an early detection of breast cancer in female adolescents at SMA N 5 Yogyakarta. This type of research is pre-experimental with one group pre-post test design. The population of this study was all female adolescents at SMA N 5 Yogyakarta. Respondents in this study were 30 female adolescents in grade XI. The sampling technique used was convenience sampling, namely by lottery. The instrument used was a knowledge questionnaire. The data analysis technique used the Shapiro Wilk test and the test used was the Wilcoxon test. The average knowledge score of female adolescents in grade XI of SMA N 5 Yogyakarta before being given health education using phantom media was 6.83 after being given health education using phantom media was 14.97. There was an increase in the knowledge score of 8.1. The value of the Wilcoxon test P = 0.000 (p-value = .000 &lt; 0.05). The conclusion of the study is that there is an effect of health education using phantoms on knowledge and behavior regarding breast self-examination (SADARI) as an early detection of breast cancer in female adolescents.

BackgroundMost women living in low‐ and middle‐income countries (LMICs) present with advanced‐stage breast cancer. Limitations of poor serviceable health systems, restricted access to treatment facilities, and lack of breast cancer screening programmes all likely contribute to the late presentation of women with breast cancer living in these countries. Women are diagnosed with advanced disease and frequently do not complete their care due to a number of factors, including financial reasons as health expenditure is largely out of pocket resulting in financial toxicity; health system failures, such as missing services or health worker lack of awareness on common signs and symptoms of cancer; and sociocultural barriers, such as stigma and use of alternative therapies. Clinical breast examination (CBE) is an inexpensive early detection technique for breast cancer in women with palpable breast masses. Training health workers from LMICs to conduct CBE has the potential to improve the quality of the technique and the ability of health workers to detect breast cancers early.ObjectivesTo assess whether training in CBE affects the ability of health workers in LMICs to detect early breast cancer.Search methodsWe searched the Cochrane Breast Cancer Specialised Registry, CENTRAL, MEDLINE, Embase, the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) search portal, and ClinicalTrials.gov up to 17 July 2021.Selection criteriaWe included randomised controlled trials (RCTs) (including individual and cluster‐RCTs), quasi‐experimental studies and controlled before‐and‐after studies if they fulfilled the eligibility criteria.Data collection and analysisTwo review authors independently screened studies for inclusion, and extracted data, assessed risk of bias, and assessed the certainty of the evidence using the GRADE approach. We performed statistical analysis using Review Manager software and presented the main findings of the review in a summary of findings table.Main resultsWe included four RCTs that screened a total population of 947,190 women for breast cancer, out of which 593 breast cancers were diagnosed. All included studies were cluster‐RCTs; two were conducted in India, one in the Philippines, and one in Rwanda. Health workers trained to perform CBE in the included studies were primary health workers, nurses, midwives, and community health workers. Three of the four included studies reported on the primary outcome (breast cancer stage at the time of presentation). Amongst secondary outcomes, included studies reported CBE coverage, follow‐up, accuracy of health worker‐performed CBE, and breast cancer mortality. None of the included studies reported knowledge attitude practice (KAP) outcomes and cost‐effectiveness.Three studies reported diagnosis of breast cancer at early stage (at stage 0+I+II), suggesting that training health workers in CBE may increase the number of women detected with breast cancer at an early stage compared to the non‐training group (45% detected versus 31% detected; risk ratio (RR) 1.44, 95% confidence interval (CI) 1.01 to 2.06; three studies; 593 participants; I2 = 0%; low‐certainty evidence).Three studies reported diagnosis at late stage (III+IV) suggesting that training health workers in CBE may slightly reduce the number of women detected with breast cancer at late stage compared to the non‐training group (13% detected versus 42%, RR 0.58, 95% CI 0.36 to 0.94; three studies; 593 participants; I2 = 52%; low‐certainty evidence).Regarding secondary outcomes, two studies reported breast cancer mortality, implying that the evidence is uncertain for the impact on breast cancer mortality (RR 0.88, 95% CI 0.24 to 3.26; two studies; 355 participants; I2 = 68%; very low‐certainty evidence). Due to the study heterogeneity, we could not conduct meta‐analysis for accuracy of health worker‐performed CBE, CBE coverage, and completion of follow‐up, and therefore reported narratively using the 'Synthesis without meta‐analysis' (SWiM) guideline. Sensitivity of health worker‐performed CBE was reported to be 53.2% and 51.7%; while specificity was reported to be 100% and 94.3% respectively in two included studies (very low‐certainty evidence). One trial reported CBE coverage with a mean adherence of 67.07% for the first four screening rounds (low‐certainty evidence). One trial reported follow‐up suggesting that compliance rates for diagnostic confirmation following a positive CBE were 68.29%, 71.20%, 78.84% and 79.98% during the respective first four rounds of screening in the intervention group compared to 90.88%, 82.96%, 79.56% and 80.39% during the respective four rounds of screening in the control group.Authors' conclusionsOur review findings suggest some benefit of training health workers from LMICs in CBE on early detection of breast cancer. However, the evidence regarding mortality, accuracy of health worker‐performed CBE, and completion of follow up is uncertain and requires further evaluation.

Breast cancer (BC) is one of the most deadly cancers for the women across the world. Early detection of breast cancer is very important for life saving. Though it is one of the most critical diseases, yet permanent cure has not been developed till now. Artificial intelligence (AI) and machine learning (ML) have been playing a vital role for effective and quick detection of this disease and increasing the rate of survivals. Deep learning (DL) technologies are helping for the analysis of very important features affecting prediction and detection of serious breast cancer diseases. This research paper focuses on solving the problem of BC detection using Wisconsin Diagnosis Breast Cancer (WDBC) data set by applying different ML models after training and validation. Additionally, various types of performance metrics have been calculated and studied. Various ensemble models have also been developed for improved detection of BC. Recurrent neural network (RNN) and gated recurrent unit (GRU)-based DL models are used for feature extraction and training. Different classification models used in this literature are random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR). After classification, the majority voting and stacking ensemble models have been applied for better performances. After exhaustive simulation and analysis, the performance measures in terms of accuracy, precision, recall, F1-score and area under the curve (AUC) values of the ensembled model are 97.3%, 0.97, 0.971, 0.97 and 0.974, respectively. The proposed ensembled model gives higher performances compared to the individual ML models.