Support Vector Machine Based Diagnostic System for Breast Cancer Using Swarm Intelligence

<p>Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset.</p>

Breast cancer is the second leading cause of cancer death in women. At the same time, it is one of the most curable cancer if it could be diagnosed early. More and more researchers have confirmed that the decision tree model has a good ability to accurately diagnose. This paper presents a diagnostic method for breast cancer based on the decision tree model combined with feature selection. Experiments were conducted on different training test divisions of the Wisconsin Breast Cancer Data Set (WBCD), a common method used by researchers to diagnose breast cancer based on machine learning methods. In this paper, in order to reduce the complexity of the decision tree model, this paper proposed to delete some highly relevant features of ... After data correlation and independence tests, it finally chose the tumor thickness, cell shape consistency, single epithelial cell size and mitosis as a subset of the decision tree model. Experimental results show that the classification accuracy (94.3%) significantly outperforms the state-of-the-art method with respect to a variety of metrics.

Feature selection in breast cancer disease important and risky task for further analysis. Breast cancer is the second leading reason for death among the women. Cancer starts from breast and spread to other part of the body. People are unable to identify their disease before it become dangerous. It can be cured if the disease identified at early stage. Accurate classification of benign tumours can avoid patients undergoing unnecessary treatments. Data Analytics and machine learning methods provides framework for prognostic studies by errorless classification of data instances into relevant based on the cancer severity. In this study we have purposed a prediction model by combining artificial intelligent based learning technique with multivariate statistical method. For automation of the diagnosis process data mining plays an significant role. The data sets available in different repositories are noisy in nature. This study suggests a hybrid feature selection method to be used with PCA (Principal Component Analysis) and Artificial Neural Network (ANN). Preprocessing of data and extracting the most relevant features done by PCA. The proposed algorithm is tested by applying it on Wisconsin Breast Cancer Dataset from UCI Repository of Machine Learning Databases. In classification phase 10 fold cross validation was used. The suggested algorithm was measured against different classifier algorithms on the same database. The evaluation results of the algorithm proposed have achieved better accuracy with sensitivity and F measure comparison with others and by enhancing this concept we can provide a future scope to produce sophisticated learning models for diagnosis.

The sector that affects all the citizens as a common factor is the health sector. It has huge data and important information about patients and their health. Now it is the need of the hour to utilize those enormous data and to extract knowledge, which will be useful to all the stake holders related to electronic health records. In this paper, we have focused on feature selection techniques to gain high quality attributes to enhance the mining process. Feature selection techniques touch all sectors, which require knowledge discovery. In this study, we have made a comparison between different feature selection methods and associated machine learning algorithms on Wisconsin Breast Cancer dataset, Wisconsin Diagnostic Breast Cancer (WDBC), and Wisconsin Prognostic Breast Cancer (WPBC) of UCI Repository. The study found that fusion of classification algorithms perform better on WBCD. It also revealed that IG performs better on WBCD, whereas IG and CFS give good results on WPBC, and CA gives best results on WDBC.

Reviewing ensemble classification methods in breast cancer

Breast cancer is the most common cancer among women (43.3 incidents per 100.000 women), with the highest mortality (14.3 incidents per 100.000 women). Early detection is critical for survival. Using machine learning approaches, the problem can be effectively classified, predicted, and analyzed. In this study, we compared eight machine learning algorithms: Gaussian Naïve Bayes (GNB), k-Nearest Neighbors (K-NN), Support Vector Machine(SVM), Random Forest (RF), AdaBoost, Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). The experiment is conducted using Breast Cancer Wisconsin datasets, confusion matrix, and 5-folds cross-validation. Experimental results showed that XGBoost provides the best performance. XGBoost obtained accuracy (97,19%), recall (96,75%), precision (97,28%), F1-score (96,99%), and AUC (99,61%). Our result showed that XGBoost is the most effective method to predict breast cancer in the Breast Cancer Wisconsin dataset.

To surmount the two-class imbalanced problem existing in the breast cancer diagnosis, a hybrid method of ROSE sampling approach with Boosted C5.0 ensemble classifier (R-Boosted C5.0) is proposed. ROSE as the sampling method is utilized to balance the class distribution. Boosted C5.0 is then used as the classifier. To serve this purpose, Wisconsin Breast Cancer Dataset (WBCD), Wisconsin Diagnosis Breast Cancer (WDBC) and three imbalanced datasets have been studied. Assessing by Matthews Correlation Coefficient (MCC), the performance of proposed method on WBCD and WDBC datasets were 98.5% and 93.0%, respectively. The experimental results show that the proposed work outperforms in contrast with the rest of the classifiers. It can be used as a clinical decision support system to assist breast cancer prediction. In practice, the proposed methodology can be further applied to class imbalanced data classification.

The effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Breast cancer is becoming a leading cause of death among women in the whole world; meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. This paper presents a hybrid intelligent system for recognition of breast cancer tumors. The proposed system includes two main modules: the feature extraction module and the predictor module. In the feature extraction module, rough set theory is used to preprocess the attributes on condition that the important information is not lost, deletes redundant attributes and conflicting objects from decision table. In the predictor module, a combined classifier is proposed based on K-nearest neighbor classifier. Experiments have been conducted on a widely used Wisconsin breast cancer dataset taken from University of California Irvine. Experimental results show that the proposed hybrid system can improve the rate of correct diagnosis of cases. The proposed combined classifier with rough set-based feature selection achieves 99.41 % classification accuracy and uses only 4 features which is the best shown to date. Different performance metrics are used to show the effectiveness of the proposed hybrid system. With these results, the proposed method is very promising compared to the previously reported results and can be used confidently for other breast cancer diagnosis problems.

Breast cancer remains one of the leading causes of mortality worldwide, underscoring the need for timely and accurate diagnosis to facilitate effective treatment. This study explores the application of machine learning based feature selection techniques to improve the identification of breast cancer using the Wisconsin Breast Cancer Dataset. The dataset comprises 569 instances and 32 attributes. After removing one non-informative attribute, the final dataset retained 30 features and one target label. The target variable, originally labeled as malignant or benign was converted into a binary format (1 for malignant and 0 for benign). To enhance model interpretability and reduce feature redundancy, three feature selection algorithms Relief, LASSO, and Fast Conditional Mutual Information (FCMIM) were employed to identify the most relevant features. An Artificial Neural Network (ANN) classifier was trained using the selected features to differentiate between malignant and benign cases. The model’s performance was evaluated using accuracy, sensitivity, specificity, and the Matthews Correlation Coefficient (MCC). The results demonstrate that feature selection significantly improves classification performance and computational efficiency. This framework underscores the potential of machine learning in oncology, offering a scalable and accurate solution for early breast cancer detection and enhanced diagnostic decision-making.

The association between cardiovascular disease and breast and gynecologic cancers among black female patients

BACKGROUND: Germline mutations in PALB2, BRCA2 and STK11 are well established as increasing risk of both breast and pancreatic cancer. More recently, ATM and BRCA1 mutations have also been associated with risk, but literature is limited. We investigated the prevalence of pathogenic mutations and likely pathogenic variants (“mutations”) in BRCA1/2, PALB2, STK11 and ATM, comparing mutation occurrence in individuals with diagnoses of breast cancer alone to those with both breast and pancreatic cancer primaries. Prevalence of CDKN2A (p16) mutations was also evaluated in the breast–pancreatic cohort because of its contribution to hereditary pancreatic cancer. METHODS: Clinical histories and test results were reviewed for patients undergoing multi-gene panel testing at one clinical laboratory between April 2012 and June 2015. The study population was limited to women with breast cancer only (n=27,573) and women with both breast and pancreatic cancer (n=97) without other primaries. Patients underwent comprehensive analysis of 5-49 genes, depending on the panel ordered. Demographic and clinical information was provided by clinicians on test requisition forms and pedigrees/clinic notes if provided. Gene-specific mutation frequencies were compared between women with breast cancer only and women with breast and pancreatic cancer using Fisher's exact test. RESULTS: Mutations were identified in BRCA1, BRCA2, PALB2 or ATM in 13 of the 97 breast - pancreatic cancer probands (13.4%) and 1,255 of the 27,573 breast cancer probands (4.6%). Gene-specific mutation frequencies and statistical comparisons may be found in Table 1. ATM mutations were significantly more likely to be identified in women with breast and pancreatic cancer compared to breast cancer alone (Table 1). Interestingly, no CDKN2A or STK11 mutations were identified in the breast plus pancreatic cohort, although this may have been limited by the small number of individuals tested for this gene. Of those 13 women with breast and pancreatic cancers who had identified mutations, 11 (85%) had diagnoses of breast cancer over age 50. Table 1. Mutation FrequenciesGeneMutation Frequence n/N (%)Mutation Frequence n/N (%)pOR (95% PI) Breast & PancreaticBreast Only ATM6/89 (6.74%)209/17,570 (1.19%)0.000766.00(2.12,13.85)BRCA12/90 (2.22 %)429/26,336 (1.63%)0.661.37(0.16,5.14)BRCA24/90 (4.44%)442/26,336 (1.68%)0.0662.72 (0.72,7.28)CDKN2A0/54 (0.00%)13/3,965 (0.33%)10.00 (0, 24.70)PALB21/891.12% 175/17,570 (1%)0.591.13(0.03, 6.54)STK110/860.00% 0/16,931 (0.00)1Inf (0, Inf) CONCLUSION: This exploratory study substantiates the association of deleterious germline ATM mutations with predisposition to both breast and pancreatic cancers. These results also suggest that mutations in ATM may account for a larger portion of inherited breast and pancreatic cancer kindreds than mutations in other well-described genes such as BRCA2, PALB2 and STK11. A personal history of breast and pancreatic cancer may warrant the expansion of current NCCN testing criteria as a single indicator for germline testing, and that pancreatic screening consortia (CAPS) consider inclusion of ATM mutations in screening recommendations. Citation Format: Gordon OK, Childers K, McFarland R, Laduca H. ATM mutations contribution to hereditary breast-pancreatic cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P3-08-07.

Background: The merging of machine learning and deep learning approaches offers a viable route for improving breast cancer prediction in this era of advanced technology and data-driven healthcare. This study focused on the use of supervised machine learning and deep learning algorithms for breast cancer prediction using Wisconsin breast cancer dataset. Methods: The features from this dataset were computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. Supervised machine learning algorithms, such as logistic regression, random forests, and support vector machines were compared with deep learning algorithms like convolutional and recurrent neural networks (RNN and CNN) for the prediction of breast cancer. The techniques combine clinical, genomic, and image-based data, that both aid in early diagnosis and individualized treatment suggestions. Results: The results of model comparison were very promising, with models obtaining excellent accuracy, sensitivity, and specificity in breast cancer diagnosis. The study used a pair plot and different classifiers with their accuracy scores on test dataset to determine which machine learning model was most suitable based on the relationship of variables represented in the plot. Support vector machine (SVM) has the highest accuracy of 98.8% and Artificial Neural Network (ANN) has lowest accuracy of 95.80% using confusion matrix and the accuracy metric. Conclusion: The study revealed that Support Vector Machine (SVM) has the highest prediction capability for Breast Cancer using Supervised Machine Learning and Deep Leaning. The new classifier performed better than the existing breast cancer prediction models in the literature.

Background: Our team has shown that younger versus older women, and both Blacks and Hispanics breast cancer (BC) survivors versus White women have a higher mortality after a second cancer. We have also shown that county-level neighborhood poverty is associated with increased cancer mortality after a BC diagnosis. In this study, we assess to what extent neighborhood-level socioeconomic status (SES) contributes to high BC mortality after second cancers and compare the association to BC mortality after a first cancer. We hypothesize that social determinants are critical to address in women who develop a second cancer despite now being part of the health system. Methods: Women diagnosed with a first BC and/or second cancer after first BC between 2007 and 2019 were identified from the Maryland (MD) Cancer Registry. Census tract-level neighborhood SES environments for poverty and median income were determined using local spatial autocorrelation analysis with Local Moran’s I of MD’s five-year census-tract estimates from the American Community Survey. MD census tract estimates above or below the state mean were identified as high (H)- or low (L)- tracts, respectively. We defined four neighborhood environments for each SES indicator as H-tracts among H-tracts (HH-tracts); L-tracts among L-tracts (LL-tracts); H-tracts among LL-tracts (HL-tracts); L-tracts among HH-tracts (LH-tracts). Women at least 20 years of age diagnosed with a local or regional primary BC, received surgery, and survived more than 365 days were assigned a tract-level neighborhood SES environment for each SES indicator associated with their first and second BC diagnosis. Time-to-event analyses were used to compare BC deaths across each tract-level SES indicator neighborhood environment separately. The data was analyzed using Multivariate Cox proportional hazard regression models adjusted for age at diagnosis, tract median income, cancer stage, treatment, and Estrogen Receptor status. Models were also stratified by race/ethnicity (Non-Hispanic White, Non-Hispanic Black), age at diagnosis (<50, 50-69, =>70), and treatment (hormone therapy and chemotherapy). Results: The analytical population included 15,149 MD women diagnosed with early-stage BC, of which 2,436 developed a second cancer, and BC deaths after first BC and second cancer diagnosis was 2039 and 315, respectively. We detected a 66% (HR=1.66, 95%CI=1.09, 2.53), 14% (HR=1.14, 95%CI=0.66,1.97), and 15% (HR=1.15, 95%CI=0.73,1.81) increase in BC death for women in LL, LH, and HL median income census tracts when compared to HH median income census tracts in the neighborhood income model after second cancer diagnosis. A 30% (HR=1.30, 95%CI=0.89, 1.90), 31% (HR=1.31, 95%CI=0.90, 1.93), and 20% (HR=1.20 95%CI=0.80, 1.79) increase in BC deaths for women in HH, LH, and HL poverty census tracts when compared to LL poverty census tracts was observed in the neighborhood poverty model after second cancer diagnosis. Notably, we observed increased BC death for women in LL, LH, and HL income neighborhood environments as well as in HH and HL poverty neighborhood environments at first BC diagnosis though not as robust as the second cancer neighborhood environment observations. Finally, neighborhood-level SES environments at first BC diagnosis were not statistically associated with BC death upon second diagnosis. Conclusion: Overall, our findings highlight the importance of neighborhood-level SES, such as poverty and median income, at the time of second cancer diagnosis as a strong indicator of BC mortality after second cancer, possibly even more than after a first BC. These findings provide initial evidence of the need for continued surveillance of neighborhood-level SES factors after a first BC diagnosis, and early interventions to improve survival among this group of high-risk women. Citation Format: Ifeoma Nwigwe, Michael Desjardins, Kala Visvanathan. The impact of neighborhood-level social determinants on breast cancer mortality after a second primary breast cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P5-04-03.

Breast cancer (BC) and genital organ cancers (GOC) are known to have a major impact on the quality of life of patients. The aim of this study was to analyze the risk of depression, anxiety, and adjustment disorders in women in Germany with a suspected but unconfirmed diagnosis of BC or GOC in their medical history. This study included women who received a suspected diagnosis of BC or GOC and were followed between 2007 and 2015 (index date). These women were matched (1:1:1) by age to women with a confirmed diagnosis of BC or GOC and women without a cancer diagnosis. The main outcome measure of the study was the rate of depression, anxiety, and adjustment disorder diagnoses within 3years of the index date. The present analysis included a total of 4,842 patients (mean age=49.3years). Within 3years of the index date, 23.5% of women with a confirmed diagnosis of BC or GOC, 14.1% of those with a suspected diagnosis of BC or GOC, and 10.5% of those without a cancer diagnosis developed depression, anxiety, or an adjustment disorder (log-rank p value <0.001). Women with a suspected diagnosis of cancer were at a higher risk for these psychiatric conditions than those without a cancer diagnosis (BC and GOC: HR 1.32; BC: HR 1.21; GOC: HR 1.50). A suspected diagnosis of BC or GOC in a woman's medical history is associated with an increased risk of developing depression, anxiety, and adjustment disorders.

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.