Optimized Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm

<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>

Accurate rainfall forecasting is crucial in activities such as agricultural planning, water resource management, and flood preparedness, so failure to accurately forecast rainfall might undermine these activities. This study aimed to improve the accuracy of daily rainfall prediction in Hai district, Kilimanjaro region, Tanzania, a region characterised by a bimodal rainfall pattern, by comparing rainfall prediction accuracies of Traditional Machine Learning (TML) and Deep Learning (DL) models. The 25-year daily rainfall dataset for Hai district was first pre-processed by cleaning, normalisation, and splitting into training (first 60%), validation (next 20%), and test (last 20%) sets. TML models (K-Nearest Neighbours (KNN) through its variant KNN Regressor and Support Vector Machine (SVM) through its variant Support Vector Regressor (SVR)) were developed, trained, and their performances compared with developed and trained DL models (Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)). Each model’s test performance was evaluated using the Root Mean Squared Error (RMSE) metric. The findings revealed that the DL models, LSTM and GRU, achieved test RMSE scores of 6.35 mm and 6.49 mm, respectively, while the TML models, KNN and SVM, achieved test RMSE scores of 6.61 mm and 10.42 mm, respectively. The study demonstrated that DL models are more effective for accurate rainfall forecasting than TML models in regions with complex rainfall patterns. The insights gained will guide the development of more reliable, location-specific weather prediction systems, ultimately supporting informed decision-making in agriculture, environmental management, and flood risk reduction in Tanzania and other regions facing similar climatic challenges

Breast cancer is a major health issue for women all over the world. Effective care and better patient outcomes depend on early identification and precise risk prediction. Ensemble learning algorithms and merging large data have recently emerged as potential ways to predict and classify breast cancer risk. This paper aims to investigate the feasibility of using ensemble learning and big data fusion for risk prediction and categorization of breast cancer. The key focus is building a reliable and precise model by combining the best features of various learning algorithms and a variety of vast and varied data sets. This research presents an Improved XGBoost Ensembling (I-XGBoost) technique with big data analytics to predict and diagnose breast cancer cells. The proposed work considers three critical identification phases to achieve greater accuracy: data pre-processing, feature extraction, and target role. The Wisconsin breast cancer diagnostic data was used for testing the proposed method. It is also compared in terms of performance with other classification methods like Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machines, Adaboost, and XGBoost. This research aims to determine which characteristics are most beneficial in predicting cancer as malignant or benign. We show that I-XGBoost has an impressively high accuracy score of 99.84% using Spark’s Python Application Programming Interface (API). This research emphasizes the promise of ensemble learning and big data fusion for predicting and classifying breast cancer risks. By proposing a new method that draws on the best features of several models and uses the wealth of information available in big data, it adds to the current body of knowledge. Positive findings highlight the practical use of the suggested paradigm in clinical contexts.

At the moment, the most prevalent form of cancer diagnosed in women across the globe is breast cancer. It develops in the breast tissue and is one of the most frequent causes of women's death. This cancer can be cured if it is diagnosed at preliminary stage. Malignant and benign are two types of tumor found in case of breast cancer. Malignant tumors are deadly as their rate of growth is much higher than benign tumors. So, early identification of tumor type is pivotal for the appropriate treatment of a patient having breast cancer. In this work, Wisconsin Breast Cancer Dataset has been used which was collected from UCI repository. Our goal is to analyze the dataset and evaluate the performance of various machine learning algorithms for predicting breast cancer. Here, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Tree, Naive Bayes and Random Forest classifiers have been implemented for classifying tumors into benign and malignant. The accuracy of each algorithm is calculated and compared to find the most suitable one. Based on the analysis, Random Forest and Support Vector Machine outperform other classifiers with accuracy of 96.5%. These classifiers can be used to build an automatic diagnostic system for preliminary diagnosis of breast cancer.

Data reduction is an indispensable part of pattern classification processes in many cases. If the number of samples is excessive, sample reduction or data reduction algorithms can be used for an effective processing time and reliable successive results. Many methods have been used for data reduction. Fuzzy c-means is one of these methods and it is widely used in such applications as clustering algorithms. In this study, we applied a different clustering algorithm, an artificial immune system (AIS), for the data reduction process. We realized the performance evaluation experiments on the standard Chainlink and Iris datasets, while the main application was conducted using the Wisconsin Breast Cancer and Pima Indian datasets, which were taken from the University of California, Irvine Machine Learning Repository. For these datasets, the performance of the AIS in the data reduction process was compared with the fuzzy c-means clustering algorithm, in which a multilayer perceptron artificial neural network was used as a classifier after the data reduction processes. The obtained results show that the maximum classification accuracies were obtained as 73.96 % for the Pima Indian Diabetes dataset and 97.80% for the Wisconsin Breast Cancer dataset with the AIS and the compression rates were 80% and 40% for these results. For fuzzy c-means clustering, however, the aforementioned accuracies were obtained as 63% and 86.69% for the Pima Indian Diabetes and Wisconsin Breast Cancer datasets, respectively. Moreover, the compression rates for these results for fuzzy c-means were 90% and 70%. When the mean classification accuracy values over the experimented compression rates were taken into consideration, the AIS reached a mean classification accuracy of 70.07% for the Pima Indian Diabetes dataset, while 47.64% was obtained by fuzzy c-means for this dataset. For the Wisconsin Breast Cancer dataset, however, the mean classification accuracies of the AIS and fuzzy c-means methods were recorded as 94.90% and 75.43%, respectively.

The most severe disease nowadays is breast cancer. The abnormal development of breast cells is what causes breast cancer. The breast ducts may include this type of malignancy. Women over 40 are the ones who get diagnosed with breast cancer most frequently. Women over 50 have the highest incidence (about 77% of invasive cases). A clear understanding of breast cancer and its kinds, as well as the creation of prophylactic measures, have made significant strides. Many genes have been linked to breast cancer. Malignant and benign cells both fall within this category. Cancerous malignant conditions exist. Non-cancerous benign cancer. Malignant cells affect other cells of the body as well as the tissues continue to divide uncontrollably. While benign tumors are significantly less dangerous than malignant ones since they do not spread to other parts of the body. Treatment is not necessary for these cancers. Malignant tumors can be treated with chemotherapy, radiation therapy, and immunotherapy, among other forms of treatment. The algorithms used to develop this prediction system are machine learning-based. Machine learning became a research focus and was found to be an effective method. Recently, many scholars have become interested in the developing technique of machine learning. Scikit-learn is used to create a model that categorizes breast cancer as either malignant or benign. A type of library that is included with the collab platform is called Scikit-learn. Mammograms have historically been the primary method of finding breast cancer. Any calcium deposits or calcifications in the breasts might be seen on a mammogram. Considering how pricey it is, few people can afford it. It will be great if an early diagnosis occurs so that treatment can get underway right away. Many women's lives could be saved by this. This dataset analysis will benefit from the use of machine learning algorithms. Six machine learning (ML) methods are compared in this paper: Naive Bayes (NB), Random Forest (RT), K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT). These methods will be applied to accurately forecast outcomes. Due to early detection, the 5-year survival rate for breast cancer patients is above 82 %. The eventual goal of this project is to assist clinicians in analyzing enormous databases of available cancer data and discovering patterns using patient data and that cancer data. This endeavor to a modest stride in the protracted battle against 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.

Rationale and ObjectivesThe human epidermal growth factor receptor 2 (HER2) gene status is crucial for determining treatment efficacy. This study assessed preoperative HER2 classification in breast cancer using machine learning based on clinicopathological and MRI characteristics.Materials and MethodsThis retrospective study involved 1,015 patients (1,030 lesions) across two centers. Patients were divided into training, internal validation, and external validation sets. Nomograms were developed using clinicopathological and MRI features. Predictive models were constructed using decision trees (DT), support vector machines (SVM), k-nearest neighbors (k-NN), artificial neural networks (ANN), and multivariable logistic regression (LR). Model performance was evaluated using receiver operating characteristic curves, decision curve analysis, and calibration curves. Model interpretability was achieved by developing nomograms and employing SHAP (SHapley Additive exPlanations) analysis.ResultsKey variables for distinguishing HER2-positive from HER2-negative cases included regional N category, estrogen receptor, PR (progesterone receptor) status, Ki-67 status, lesion number, distribution quadrant, and accompanying signs. The SVM model achieved the highest AUC of 0.86 (95% confidence interval (CI): 0.81–0.90) in the training set, while the ANN model had an AUC of 0.77 (95% CI: 0.67–0.86) in the internal validation set. In the external validation set, the LR model achieved the highest AUC of 0.66 (95% CI: 0.56–0.76), although the overall performance was modest. For HER2-low versus HER2-zero differentiation, Ki-67 status, lesion number, distribution quadrant, mass shape, early enhancement rate, and ADC (apparent diffusion coefficient) were significant. The SVM model attained the highest AUC of 0.87 (95% CI: 0.83–0.91) in the training set, while the LR model demonstrated superior generalizability, yielding the highest AUCs in both the internal and external validation sets (internal: 0.67, 95% CI: 0.58–0.76; external: 0.74, 95% CI: 0.65–0.83). Radiologists benefited from the nomogram for improved diagnostic accuracy, especially junior radiologists. SHAP analysis revealed that PR status was paramount for HER2-positive classification, whereas mass shape and ADC values were dominant for identifying HER2-low status.ConclusionIntegrating machine learning with clinicopathological and MRI characteristics improves the accuracy of HER2 status classification in breast cancer and enhances diagnostic capabilities for radiologists in clinical practice.

Breast Cancer (BC) is counted as one of the most fatal diseases responsible for mortality in women. BC feels like a lump that can be felt different than other BC tissues. Cancer cells spread to other parts of the body by getting into the bloodstream or lymphatic system, which results in BC. It becomes crucial to develop and analyze methods to help understand BC and its trend affecting women. In this paper, we discussed different models to analyze BC tumors and classified them into benign and malignant. Benign tumors are referred to as ‘non-cancerous’ by many, and those that are ‘cancerous’ as malignant. The analysis was done on Wisconsin Breast Cancer Dataset (WBCD). We performed Logistic Regression (LR), Decision Tree(DT), Random Forest(RF), K-Nearest Neighbor (KNN), and Artificial Neural Network(ANN) on the dataset and compared their results. Our ANN model yielded the best result among many. All models were compared on various metrics which are Accuracy score, F1 score, Precision score, and Recall score.

Child mortality is a big problem around the world, especially in low- and middle-income nations where there are big differences in health care and social conditions. This investigation seeks to create a predictive model for child mortality and pinpoint the key factors that significantly contribute to it, employing machine learning (ML) methodologies. The dataset includes various features such as parental age, maternal education, birth weight, wealth index, and access to healthcare services. Thirteen machine learning classifiers were used, categorized into four model groups: Traditional Models (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Naive Bayes), Tree-Based Models (Decision Tree, Random Forest, Extra Trees), Boosting Models (AdaBoost, Gradient Boosting, XGBoost), and Ensemble Learning Models (Soft Voting, Hard Voting, Stacking). The efficacy of each model was assessed using classification metrics, including Accuracy, Precision, Recall, and F1-Score within a 10-fold cross-validation framework to guarantee robustness. Results indicate that ensemble models, particularly AdaBoost, achieved the highest predictive accuracy, with perfect scores across all metrics (1.00). XGBoost and Stacking also demonstrated strong and consistent performance. The findings indicate that ensemble learning methods are effective in predicting child mortality and can assist policymakers and healthcare planners in identifying high-risk populations and implementing targeted interventions to reduce child mortality.

Breast cancer is the most common malignant tumor found in women, and there is no cure for advanced breast cancer. Early detection and treatment can effectively improve patient survival. This paper uses five machine learning classification models, namely Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbors Algorithm (KNN). The training data for the five models are provided by the Wisconsin Breast Cancer Dataset (WBCD). By evaluating and comparing the performance of the five models in accuracy, F1Score, ROC curve, and PR curve, the study finds that LR has the best performance.

According to recent statistics, breast cancer is one of the most prevalent cancers among women in the world. It represents the majority of new cancer cases and cancer-related deaths. Early diagnosis is very important, as it becomes fatal unless detected and treated in early stages. With the latest advances in artificial intelligence and machine learning (ML), there is a great potential to diagnose breast cancer by using structured data. In this paper, we conduct an empirical comparison of 10 popular machine learning models for the prediction of breast cancer. We used well known Wisconsin Breast Cancer Dataset (WBCD) to train the models and employed advanced accuracy metrics for comparison. Experimental results show that all models demonstrate superior accuracy, while Support Vector Machines (SVM) had slightly better performance than other methods. Logistic Regression, K-Nearest Neighbors and Neural Networks also proved to be strong classifiers for predicting breast cancer.

Reviewing ensemble classification methods in breast cancer

ความเป็นมา การตอบสนองทางพยาธิวิทยาภายหลังการได้รับยาเคมีบำบัดก่อนผ่าตัดมีความสัมพันธ์กับผลลัพธ์ทางคลินิก ปัจจุบันไม่มีปัจจัยใดที่สามารถทำนายการตอบสนองต่อยาเคมีบำบัดในมะเร็งเต้านมชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทูได้อย่างแม่นยำ เช่นเดียวกับ Ki-67 การแสดงออกของโปรตีน MCM2 ซึ่งมีหน้าที่ควบคุมการสังเคราะห์ดีเอ็นเอในระดับสูงบ่งชี้ถึงการแบ่งตัวของเซลล์ที่รวดเร็ว และมีความสัมพันธ์กับการพยากรณ์โรคที่ไม่ดีในมะเร็งหลายชนิดรวมถึงมะเร็งเต้านม การศึกษานี้มีวัตถุประสงค์เพื่อศึกษาการแสดงออกของ MCM2 ในชิ้นเนื้อก่อนได้รับยาเคมีบำบัดเพื่อทำนายการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I ในมะเร็งเต้านมระยะต้นชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทู วิธีการศึกษา ทบทวนเวชระเบียนอิเล็กทรอนิกส์ของผู้ป่วยมะเร็งเต้านมระยะต้นชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทูที่ได้รับยาเคมีบำบัดก่อนผ่าตัดในโรงพยาบาลจุฬาลงกรณ์ ระหว่าง 1 มกราคม 2558 - 31 ธันวาคม 2563 นำชิ้นเนื้อก่อนได้รับยาเคมีบำบัดตรวจการแสดงออกของ MCM2 ด้วยการย้อมอิมมูโนฮิสโตเคมีและทบทวนลักณะทางพยาธิวิทยาเพื่อคำนวน RCB index ในชิ้นเนื้อหลังได้รับยาเคมีบำบัด คำนวนการติดสีของ MCM2 ด้วยโปรแกรมวิเคราะห์ภาพ Aperio Imagescope ผลการวิจัย ผู้ป่วยทั้งหมด 88 คน มีค่ามัธยฐานอายุ 49 ปี (พิสัย 27-80 ปี) 61.4% เป็นวัยก่อนหมดประจำเดือน มีระยะโรคมะเร็งทางคลินิก cT4 และ cN1 40% และ 66.7% ตามลำดับ เป็นมะเร็งเต้านมชนิด invasive ductal carcinoma 91.1% มีระดับทางพยาธิวิทยาระดับที่ 2 58.9% มีการแสดงออกของ ER >10% 92.2% และสถานะ PR เป็นลบ 22.2% ในชิ้นเนื้อมะเร็งเต้านมทั้งหมด 72 ชิ้นที่สามารถย้อม MCM2 และประเมิน RCB index ได้ พบว่าการแสดงออกของ MCM2 >=40% มีความสัมพันธ์กับการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I อย่างมีนัยสำคัญทางสถิติจากการวิเคราะห์แบบ univariate analysis (OR = 18.33 ช่วงความเชื่อมั่น 95% = 1.88-178.98 p-value = 0.012) ปัจจัยทางพยาธิวิทยาคลินิกที่พบว่ามีความสัมพันธ์กับ MCM2 >=40% คือ ระดับของพยาธิวิทยาระดับที่ 3 การแสดงออกของ ER <=10% สถานะ PR เป็นลบ และ Ki-67 >=20% แต่ในการวิเคราะห์แบบ multivariate analysis ไม่มีปัจจัยทางพยาธิวิทยาคลินิกใดสัมพันธ์กับการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I ลักษณะทางอิมมูโนฮิสโตเคมีก่อนและหลังให้ยาเคมีบำบัดมีลักษณะโดยรวมใกล้เคียงกัน ค่ามัธยฐานของการปลอดโรค และอัตราการปลอดโรคที่ 3 ปี ไม่มีความแตกต่างกันอย่างมีนัยสำคัญทางสถิติระหว่างผู้ป่วยที่มีการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I และ RCB II/III สรุปผลการวิจัย การแสดงออกของ MCM2 >=40% ด้วยการตรวจทางอิมมูโนฮิสโตเคมี ในชิ้นเนื้อก่อนได้รับยาเคมีบำบัดของผู้ป่วยมะเร็งเต้านมระยะต้นชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทู มีความสัมพันธ์กับการตอบสนองแบบ RCB 0/I การตรวจการแสดงออกของ MCM2 อาจช่วยทำนายประโยชน์ของยาเคมีบำบัดก่อนผ่าตัดในผู้ป่วยกลุ่มนี้ได้ อย่างไรก็ตามควรมีการศึกษาเพิ่มเติมในประชากรที่มีขนาดใหญ่ขึ้นเพื่อยืนยันผลของการศึกษานี้

Cancer is one of the leading causes of death, and breast cancer is the second leading cause of cancer death in women. One method to realize the level of malignancy of breast cancer from an early age is by classifying the cancer malignancy using data mining. One of the widely used data mining methods with a good level of accuracy is the Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). Evaluation techniques of percentage split and cross-validation were used to evaluate and compare the SVM and KNN classification models. The result was that the accuracy level of the SVM classification method was better than the KNN classification method when using the cross-validation technique, which is 95,7081%. Meanwhile, the KNN classification method was better than the SVM classification method when using the percentage split technique, which is 95,4220%. From the comparison results, it can be seen that the KNN and SVM methods work well in the classification of breast cancer.