Breast Cancer Dataset Research Articles

Performance Analysis of a new Filter and Wrapper Sequence for the Survivability Prediction of Breast Cancer Patients

Feature selection is an essential preprocessing step for removing redundant or irrelevant features from multidimensional data to improve predictive performance. Currently, medical clinical datasets are increasingly large and multidimensional and not every feature helps in the necessary predictions. So, feature selection techniques are used to determine relevant feature set that can improve the performance of a learning algorithm. This study presents a performance analysis of a new filter and wrapper sequence involving the intersection of filter methods, Mutual Information and Chi-Square followed by one of the wrapper methods: Sequential Forward Selection and Sequential Backward Selection to obtain a more informative feature set for improved prediction of the survivability of breast cancer patients from the clinical breast cancer dataset, SEER. The improvement in performance due to this filter and wrapper sequence in terms of Accuracy, False Positive Rate, False Negative Rate and Area under the Receiver Operating Characteristics curve is tested using the Machine learning algorithms: Logistic Regression, K-Nearest Neighbour, Decision Tree, Random Forest, Support Vector Machine and Multilayer Perceptron. The performance analysis supports the Sequential Backward Selection of the new filter and wrapper sequence over Sequential Forward Selection for the SEER dataset.

Deep Learning based Improved Generative Adversarial Network for Addressing Class Imbalance Classification Problem in Breast Cancer Dataset

The breast cancer diagnosis is one of the challenging tasks of medical field. Especially, the breast cancer diagnosis among younger women (under 40 years old) is more complicated, because their breast tissue is generally denser than the older women. The Breast Cancer Wisconsin image dataset contains two classes: (i) Benign (Minority class), (ii) Malignant (Majority class). The imbalanced class distribution leads to a deterioration in the classifier model performance owing to the biased classification towards the majority class. Therefore, in this article, an improved generative adversarial network (I-GAN) is proposed to overcome the class imbalance problem. Here, the proposed method is the consolidation of deep convolutional generative adversarial network (DCIGAN) and modified convolutional neural network, (MCNN), therefore it is known as DCIGAN-MCNN method. First, the DCIGAN is utilized for balancing the dataset by generating more samples in the training dataset. Then, this training dataset based the classification of Breast cancer is developed using the modified convolutional neural network. The proposed method is executed in MATLAB. The performance analysis are carried out in Breast Cancer Wisconsin (Prognostic) Data Set provides Maximum Geometry Mean (MGM) as 24.058%, 9.582%, Matthews’s correlation coefficient (MCC) as 78.623%, 30.357% higher than the existing methods, like CI-BC-RK-SVM, CI-BC-GA, CI-BC-DC-CNN, CI-BC-RF and CI-BC-BMIC-Net respectively. Finally, the simulation results prove that the proposed method can be able to find the optimal solutions efficiently and accurately.

Breast Cancer Image Semantic Segmentation with Attention U-Net

Semantic segmentation is to segment objects in an image into meaningful units. Among them, the basic idea of U-Net is to use low-dimensional as well as high-dimensional information to extract image features and enable accurate location identification. In this paper, we present a new model that combines Attention Gates with U-Net and evaluate the results through semantic segmentation with breast cancer datasets. To this end, this study proposes and tests a methodology for breast cancer image segmentation based on Attention U-Net. In conclusion, when comparing the performance with the existing U-Net, It can be seen that IoU is 0.069 higher than the existing U-Net. Thus, the proposed model enables better image semantic segmentation.

Multi-omic subtyping and molecular determinants of neoadjuvant therapeutic regimen for locally advanced HER2-positive and triple-negative breast cancer: Implications of novel therapy.

e12600 Background: Neoadjuvant chemotherapy is the backbone treatment modalities for BC patients for locally advanced breast cancer (BC) improves R0 resection rate. Determinants of differentially therapeutic response to anthracycline or platinum-based neoadjuvant therapy were largely unknown but were urgently needed to maximize patients’ benefit. Methods: A total of 149 local advanced (cT2-3, cN0-2, cM0-1) BC patients (HER2 positive BC, n = 54 and TNBC, n = 95) were retrospectively enrolled in the study, whole-exon sequencing (WES), mRNA sequencing and (phosphor-)proteomics were systematically conducted and comprehensively analyzed. The primary end point of the study was the pathological complete response (pCR) rate. The Cancer Genome Atlas (TCGA) BC dataset and GSE130787 set were used to validate our analysis. Cancer cell line encyclopedia (CCLE) dataset was utilized to screen putative small molecular inhibitors to BC cells with molecular features of resistance. Results: Through integrated analysis of multi-omics data, three subtypes with distinct molecular features were identified: immune-activated (IA) subtype, vesicular transport pathways activation (VTs) subtype, and kinase activation (KA) subtype. Same scenario of molecular subtypes can be repeated in TCGA dataset and prognosis of these subtypes can be differentiate ( p= 0.02). Patients with IA subtype majorly favored cisplatin-based treatment ( p= 0.005). And this phenomenon was confirmed in external dataset (GSE130787, n = 26, HER2-positive BC receiving TCbH treatment). Patients with VTs subtype, with significant enrichment of vesicular transport-related pathways, displayed anthracycline sensitivity. Anthracycline transporter, ABCB1 expression was significantly reduced in VTs subtype at both mRNA ( p < 0.001) and protein ( p < 0.05) levels. CCLE breast cancer cells with the characteristics of VTs subtype and lower ABCB1 expression also displayed the sensitivity of epirubicin ( p = 0.0037). Patient with KA subtype was insensitive to both platinum and anthracycline-based therapy. Multiple kinase pathways, especially, non-canonical TGF-β signaling pathway-related genes were overexpressed (p < 0.05), and significantly correlated with non-pCR ( p= 0.002). Phosphor-proteomic analysis indicated TGFβ1-mediated RhoA/ROCK pathway (Cofilin, RhoA kinase, MYH14 proteins) were highly activated. Eleven drugs were found in CCLE inhibiting growth of BC cell lines with upregulated non-canonical TGF-β pathway genes ( p < 0.05). Conclusions: Three multi-omic subtypes with distinct response to anthracycline or platinum-based therapy were identified. Moreover, activated non-canonical TGF-β pathway may mediate therapeutic resistance, but also indicated therapeutic vulnerabilities of several kinase inhibitors.

Feature Selection Using a Hybrid Approach Depends on Filter and Wrapper Methods for Accurate Breast Cancer Diagnosis

Breast cancer is the biggest cause of mortality in women, outscoring all other malignancies. Diagnosing breast cancer is hard because the disease is complicated, treatment methods change, and there are many different kinds of patients. Information technology and artificial intelligence contribute to improve diagnostic procedures, which are critical for care and treatment as well as reducing and controlling cancer recurrence. The primary part of this research is to develop a new feature selection strategy based on a hybrid approach that combines two methods for selecting features: the filter and the wrapper. In two stages, this method reduces the number of features from 30 to 15 to increase and improve classification accuracy. The suggested method was tested using the Wisconsin Breast Cancer Dataset dataset (WDBC). To enhance the classification of breast cancer tumors, a soft voting classifier was used in this study. The proposed methodology outperforms previous research, achieving 1 for the F1 score, 1 for AUC, 1 for recall, 1 for precision, and 100% for accuracy. Furthermore, 10-fold cross-validation has a 98.2% accuracy rate.

Comparative Study on the Performance of the Bagging Algorithm in the Breast Cancer Dataset

Breast cancer remains a predominant health concern globally. Early detection, powered by advancements in medical imaging and computational methods, plays a vital role in enhancing survival rates. This research delved into the application and performance of the Bagging algorithm on a Breast Cancer dataset that underwent image segmentation using the Canny method and feature extraction through Hu-Moments. The Bagging algorithm demonstrated moderately consistent performance across a 5-fold cross-validation, with average metrics of 56.9% accuracy, 58.3% precision, 57.7% recall, and 56.6% F-measure. While the results showcased the potential of the Bagging algorithm in classifying breast cancer data, there remains an avenue for further optimization and exploration of other ensemble or deep learning techniques. The findings contribute to the broader domain of machine learning in medical imaging and offer insights for future research directions and clinical diagnostic tool development.

Breast Cancer Detection Using Machine Learning

Abstract: Breast cancer is one of the most contagious illnesses and the second leading cause of cancer mortality in women. Early breast cancer detection improves survival rates because better care may be given. Machine learning-based data categorization has been widely employed in breast cancer diagnosis and early detection. This literature review's primary focus is the categorization of accessible data using ML for breast cancer early pin-pointing and spotting. It is clear from reading multiple publications on artificial intelligence that there are several ways for detecting cancer. This study aims to compile reviews and technical publications on breast cancer diagnosis and prognosis. It provides an overview of the current research being done on various breast cancer datasets utilising data mining approaches to improve breast cancer detection and prognosis

Breast Cancer Using Machine Learning

Abstract: Women die from breast cancer, which is an abstract concept. Breast cancer is the most important problem. The most frequent cancer in women diagnosed globally has now surpassed lung cancer in prevalence. early detection that aids in cancer prevention. If breast cancer is to have a very high survival rate, it must be found in its earliest stages. The efficient machine learning method is utilized to categorize the data. Methods are employed in the medical field to aid in diagnosis and decisionmaking. This study used the Wilcoxon breast cancer dataset to do data visualization and compare various machine learning methods, including the Support Vector Machine (SVM), Decision Trees, Naive Bayes (NB), K Nearest Neighbours (K-NN), Adaboost, Xgboost, and Random Forest. The primary goal is to assess the data's accuracy in terms of each algorithm's efficiency and effectiveness in terms of accuracy, precision, sensitivity, and specificity. Our goal is to use machine learning to detect things quickly, effectively, and precisely. The experimental findings had the lowest error rate and the best accuracy (98.24%).

Multi-task banded regression model: A novel individual survival analysis model for breast cancer

PurposeTo reveal the hazard probability of individual breast cancer patients, a multi-task banded regression model is proposed for individual survival analysis of breast cancer. MethodsA banded verification matrix is designed to construct the response transform function of the proposed multi-task banded regression model, which can solve the repeated switching of survival rate. A martingale process is introduced to construct different nonlinear regressions for different survival subintervals. The concordance index (C-index) is used to compare the proposed model with Cox proportional hazards (CoxPH) models and previous multi-task regression models. ResultsTwo commonly-used breast cancer datasets are employed to validate the proposed model. Specifically, the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) includes 1981 breast cancer patients, of which 57.7% died of breast cancer. The Rotterdam & German Breast Cancer Study Group (GBSG) includes 1546 patients with lymph node-positive breast cancer in a randomized clinical trial, of which 44.4% died. Experimental results indicate that the proposed model is superior to some existing models for overall and individual survival analysis of breast cancer, with the C-index of 0.6786 for the GBSG and 0.6701 for the METABRIC. ConclusionThe superiority of the proposed model can be contributed to three novel ideas. One is that a banded verification matrix can band the response of the survival process. Second, the martingale process can construct different nonlinear regressions for different survival subintervals. Third, the novel loss can adapt the model to making the multi-task regression similar to the real survival process.

Integration of multiomics data shows down regulation of mismatch repair and tubulin pathways in triple-negative chemotherapy-resistant breast tumors

BackgroundTriple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Patients with TNBC are primarily treated with neoadjuvant chemotherapy (NAC). The response to NAC is prognostic, with reductions in overall survival and disease-free survival rates in those patients who do not achieve a pathological complete response (pCR). Based on this premise, we hypothesized that paired analysis of primary and residual TNBC tumors following NAC could identify unique biomarkers associated with post-NAC recurrence.Methods and resultsWe investigated 24 samples from 12 non-LAR TNBC patients with paired pre- and post-NAC data, including four patients with recurrence shortly after surgery (< 24 months) and eight who remained recurrence-free (> 48 months). These tumors were collected from a prospective NAC breast cancer study (BEAUTY) conducted at the Mayo Clinic. Differential expression analysis of pre-NAC biopsies showed minimal gene expression differences between early recurrent and nonrecurrent TNBC tumors; however, post-NAC samples demonstrated significant alterations in expression patterns in response to intervention. Topological-level differences associated with early recurrence were implicated in 251 gene sets, and an independent assessment of microarray gene expression data from the 9 paired non-LAR samples available in the NAC I-SPY1 trial confirmed 56 gene sets. Within these 56 gene sets, 113 genes were observed to be differentially expressed in the I-SPY1 and BEAUTY post-NAC studies. An independent (n = 392) breast cancer dataset with relapse-free survival (RFS) data was used to refine our gene list to a 17-gene signature. A threefold cross-validation analysis of the gene signature with the combined BEAUTY and I-SPY1 data yielded an average AUC of 0.88 for six machine-learning models. Due to the limited number of studies with pre- and post-NAC TNBC tumor data, further validation of the signature is needed.ConclusionAnalysis of multiomics data from post-NAC TNBC chemoresistant tumors showed down regulation of mismatch repair and tubulin pathways. Additionally, we identified a 17-gene signature in TNBC associated with post-NAC recurrence enriched with down-regulated immune genes.

Regulation of IDO2 by the Aryl Hydrocarbon Receptor (AhR) in Breast Cancer.

Indoleamine 2,3-dioxygenase 2 (IDO2) is a tryptophan-catabolizing enzyme and a homolog of IDO1 with a distinct expression pattern compared with IDO1. In dendritic cells (DCs), IDO activity and the resulting changes in tryptophan level regulate T-cell differentiation and promote immune tolerance. Recent studies indicate that IDO2 exerts an additional, non-enzymatic function and pro-inflammatory activity, which may play an important role in diseases such as autoimmunity and cancer. Here, we investigated the impact of aryl hydrocarbon receptor (AhR) activation by endogenous compounds and environmental pollutants on the expression of IDO2. Treatment with AhR ligands induced IDO2 in MCF-7 wildtype cells but not in CRISPR-cas9 AhR-knockout MCF-7 cells. Promoter analysis with IDO2 reporter constructs revealed that the AhR-dependent induction of IDO2 involves a short-tandem repeat containing four core sequences of a xenobiotic response element (XRE) upstream of the start site of the human ido2 gene. The analysis of breast cancer datasets revealed that IDO2 expression increased in breast cancer compared with normal samples. Our findings suggest that the AhR-mediated expression of IDO2 in breast cancer could contribute to a pro-tumorigenic microenvironment in breast cancer.

A correlation graph attention network for classifying chromosomal instabilities from histopathology whole-slide images

The chromosome instability (CIN) is one of the hallmarks of cancer and is closely related to tumor metastasis. However, the sheer size and resolution of histopathology whole-slide images (WSIs) already challenges the capabilities of computational pathology. In this study, we propose a correlation graph attention network (MLP-GAT) that can construct graphs for classifying multi-type CINs from the WSIs of breast cancer. We construct a WSIs dataset of breast cancer from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA). Extensive experiments show that MLP-GAT far outperforms accepted state-of-the-art methods and demonstrate the advantages of the constructed graph networks for analyzing WSI data. The visualization shows the difference among the tiles in a WSI. Furthermore, the generalization performance of the proposed method was verified on the stomach cancer. This study provides guidance for studying the relationship between CIN and cancer from the perspective of image phenotype.

Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted.

Breast cancer is the second most common type of cancer among women, and it can threaten women's lives if it is not diagnosed early. There are many methods for detecting breast cancer, but they cannot distinguish between benign and malignant tumors. Therefore, a biopsy taken from the patient's abnormal tissue is an effective way to distinguish between malignant and benign breast cancer tumors. There are many challenges facing pathologists and experts in diagnosing breast cancer, including the addition of some medical fluids of various colors, the direction of the sample, the small number of doctors and their differing opinions. Thus, artificial intelligence techniques solve these challenges and help clinicians resolve their diagnostic differences. In this study, three techniques, each with three systems, were developed to diagnose multi and binary classes of breast cancer datasets and distinguish between benign and malignant types with 40× and 400× factors. The first technique for diagnosing a breast cancer dataset is using an artificial neural network (ANN) with selected features from VGG-19 and ResNet-18. The second technique for diagnosing breast cancer dataset is by ANN with combined features for VGG-19 and ResNet-18 before and after principal component analysis (PCA). The third technique for analyzing breast cancer dataset is by ANN with hybrid features. The hybrid features are a hybrid between VGG-19 and handcrafted; and a hybrid between ResNet-18 and handcrafted. The handcrafted features are mixed features extracted using Fuzzy color histogram (FCH), local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) methods. With the multi classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 95.86%, an accuracy of 97.3%, sensitivity of 96.75%, AUC of 99.37%, and specificity of 99.81% with images at magnification factor 400×. Whereas with the binary classes data set, ANN with the hybrid features of the VGG-19 and handcrafted reached a precision of 99.74%, an accuracy of 99.7%, sensitivity of 100%, AUC of 99.85%, and specificity of 100% with images at a magnification factor 400×.

Causal relationships between serum matrix metalloproteinases and estrogen receptor-negative breast cancer: a bidirectional mendelian randomization study

To better clarify the causal effects between matrix metalloproteinases (MMPs) and estrogen-receptor (ER)-negative breast cancer (BC), we investigated the bidirectional causal relationship between MMPs and ER-negative BC by mendelian randomization (MR) analysis. Summary statistic data of five MMPs were extracted from European participants in 13 cohorts. Data of ER-negative BC collected from one of genome-wide association studies of European ancestry was used as experimental datasets and another four ER-negative BC datasets were used as validation sets. Inverse variance weighted method was used for main MR analysis and sensitivity analysis was also conducted. Serum level of MMP-1 has negative effect on ER-negative BC (odds ratio = 0.92, P = 0.0008) but the latter one was not the cause of the former one, which was supported by validation sets. No bidirectional causal effect was detected between the other four types of MMPs and ER-negative BC (P > 0.05). Sensitivity analysis indicated robustness of the above results without remarkable bias. To conclude, serum MMP-1 may be a protective factor against ER-negative BC. No reciprocal causality was found between the other kinds of MMPs and ER-negative BC. MMP-1 was indicated as a biomarker for risk of ER-negative BC.

A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes

Genome-wide association studies (GWASs) have identified more than 200 genomic loci for breast cancer risk, but specific causal genes in most of these loci have not been identified. In fact, transcriptome-wide association studies (TWASs) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify candidate genes, we performed a GWAS analysis in a breast cancer dataset from UK Biobank (UKB) and combined the results with the GWAS results of the Breast Cancer Association Consortium (BCAC) by a meta-analysis. Using the summary statistics from the meta-analysis, we performed a joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 11 tissues that are potentially relevant to breast cancer from the Genotype-Tissue Expression (GTEx) data. In the GWAS analysis, we identified eight loci distinct from those reported previously. In the TWAS analysis, we identified 309 genes at 108 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold. Of these, 17 genes were located in eight regions that were at least 1 Mb away from published GWAS hits. The remaining TWAS-significant genes were located in 100 known genomic loci from previous GWASs of breast cancer. We found that 21 genes located in known GWAS loci remained statistically significant after conditioning on previous GWAS index variants. Our study provides insights into breast cancer genetics through mapping candidate target genes in a large proportion of known GWAS loci and discovering multiple new loci.

EfficientNets transfer learning strategies for histopathological breast cancer image analysis

Breast cancer (BC) is one of the major principal sources of high mortality among women worldwide. Consequently, early detection is essential to save lives. BC can be diagnosed with different modes of medical images such as mammography, ultrasound, computerized tomography, biopsy, and magnetic resonance imaging. A histopathology study (biopsy) that results in images is often performed to help diagnose and analyze BC. Transfer learning (TL) is a machine-learning (ML) technique that reuses a learning method that is initially built for a task to be applied to a model for a new task. TL aims to enhance the assessment of desired learners by moving the knowledge contained in another but similar source domain. Consequently, the challenge of the small dataset in the desired domain is reduced to build the desired learners. TL plays a major role in medical image analysis because of this immense property. This paper focuses on the use of TL methods for the investigation of BC image classification and detection, preprocessing, pretrained models, and ML models. Through empirical experiments, the EfficientNets pretrained neural network architectures and ML classification models were built. The support vector machine and eXtreme Gradient Boosting (XGBoost) were learned on the BC dataset. The result showed a comparative but good performance of EfficientNetB4 and XGBoost. An outcome based on accuracy, recall, precision, and F1_Score for XGBoost is 84%, 0.80, 0.83, and 0.81, respectively.

Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis.

Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset-namely CMTD-of 1056 hematoxylin and eosin JPEG images from 20 benign and 24 malignant CMTs, with three different CAD systems based on the combination of a convolutional neural network (VGG16, Inception v3, EfficientNet), which acts as a feature extractor, and a classifier (support vector machines (SVM) or stochastic gradient boosting (SGB)), placed on top of the neural net. Based on a human breast cancer dataset (i.e., BreakHis) (accuracy from 0.86 to 0.91), our models were applied to the CMT dataset, showing accuracy from 0.63 to 0.85 across all architectures. The EfficientNet framework coupled with SVM resulted in the best performances with an accuracy from 0.82 to 0.85. The encouraging results obtained by the use of DP and CAD systems in CMTs provide an interesting perspective on the integration of artificial intelligence and machine learning technologies in cancer-related research.

GLassonet: Identifying Discriminative Gene Sets Among Molecular Subtypes of Breast Cancer.

Breast cancer is a heterogeneous disease caused by various alterations in the genome or transcriptome. Molecular subtypes of breast cancer have been reported, but useful biomarkers remain to be identified to uncover underlying biological mechanisms and guide clinical decisions. Towards biomarker discovery, several studies focus on genomic alterations that provide differences, while few works concern transcriptomic characterizations that mediate tumor progression. Rather than using differential expression (DE) or weighted network analysis, we propose a feature selection method, dubbed GLassonet, to identify discriminative biomarkers from transcriptome-wide expression profiles by embedding the relationship graph of high-dimensional expressions into the Lassonet model. GLassonet comprises a nonlinear neural network for identifying cancer subtypes, a skipping fully connected layer for canceling the connections of hidden layers from input features to output categories, and a graph enhancement for preserving the discriminative graph into the selected subspace. First, an iterative optimization algorithm learns model parameters on the TCGA breast cancer dataset to investigate the classification performance. Then, we probe the distribution patterns of GLassonet-selected gene sets across the cancer subtypes and compare them to gene sets outputted from the state-of-the-art. More profoundly, we conduct the overall survival analysis on three GLassonet-selected new marker genes, i.e., SOX10, TPX2, and TUBA1C, to investigate their expression changes and assess their prognostic impacts. Finally, we perform the enrichment analysis to discover the functional associations of the GLassonet-selected genes with GO terms and KEGG pathways. Experimental results show that GLassonet has a powerful ability to select the discriminative genes, which improve cancer subtype classification performance and provide potential biomarkers for cancer personalized therapy.

A Breast Cancer Diagnosis Framework Based on Machine Learning

Breast cancer is becoming the leading cause of mortality among women. One of the most prevalent diseases in women, breast cancer is brought on by a variety of clinical, lifestyle, social, and economic variables. Predictive approaches based on machine learning offer methods for diagnosing breast cancer sooner. It may be found using a variety of analytical methods, including Breast MRI, X-ray, thermography, mammograms, ultrasound, etc. The most prevalent technique for performance evaluation uses accuracy measures, and the Convolutional Neural Network (CNN) is the most accurate and widely used model for breast cancer diagnosis. The Wisconsin Breast Cancer Datasets (WBCD) were used to evaluate the suggested method. Out of a total of 569 samples, 273 samples were chosen for this experiment as the test data, while the other samples were utilized for training and validation. The review's findings showed that the Convolutional Neural Network (CNN) is the most effective and widely used model for finding breast cancer, and that the most often used technique for judging performance is accuracy metrics. The application of deep learning to such a wide range of real-world issues is astounding.

BREAST CANCER DETECTION WITH MACHINE LEARNING APPROACH

One of the most widespread diseases among women today is breast cancer. Early and accurate diagnosis is key in rehabilitation and treatment. The usage of mammograms has some uncertainties in the detection rate. To develop tools for physicians for effective and early detection and diagnosis, machine learning techniques can be adopted. The introduction of Machine Learning (ML) in developing the tool will increase the survival rate of patients with breast cancer. This research work proposed different six ML techniques; Logistic Regression, Linear Discriminant Analysis, Decision Tree (DT), KNN, Naïve Bayes (NB), and Support Vector Machine (SVM), and then recommended the model with the highest accuracy for breast cancer detection. The experiment was carried out in a python environment and all the aforementioned techniques were validated with Wisconsin Breast Cancer dataset and evaluated with accuracy, precision, and recall.