Improving Breast Cancer Diagnosis Accuracy by Particle Swarm Optimization Feature Selection

The study presents a method for iterative parameter tuning of tree ensemble-based models using Bayesian hyperparameter tuning for states prediction, using breast cancer as an example. The proposed method utilizes three different datasets, including the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the Surveillance, Epidemiology, and End Results (SEER) breast cancer dataset, and the Breast Cancer Coimbra dataset (BCCD), and implements tree ensemble-based models, specifically AdaBoost, Gentle-Boost, LogitBoost, Bag, and RUSBoost, for breast cancer prediction. Bayesian optimization was used to tune the hyperparameters of the models iteratively, and the performance of the models was evaluated using several metrics, including accuracy, precision, recall, and f1-score. Our results show that the proposed method significantly improves the performance of tree ensemble-based models, resulting in higher accuracy, precision, recall, and f1-score. Compared to other state-of-the-art models, the proposed method is more efficient. It achieved perfect scores of 100% for Accuracy, Precision, Recall, and F1-Score on the WDBC dataset. On the SEER BC dataset, the method achieved an accuracy of 95.9%, a precision of 97.6%, a recall of 94.2%, and an F1-Score of 95.9%. For the BCCD dataset, the method achieved an accuracy of 94.7%, a precision of 90%, a recall of 100%, and an F1-Score of 94.7%. The outcomes of this study have important implications for medical professionals, as early detection of breast cancer can significantly increase the chances of survival. Overall, this study provides a valuable contribution to the field of breast cancer prediction using machine learning.

The aim of this research is to combine the feature selection (FS) and optimization algorithms as the optimal tool to improve the learning performance like predictive accuracy of the Wisconsin Breast Cancer Dataset classification. An ensemble of the reduced data patterns based on FS was used to train a neural network (NN) using the Levenberg-Marquardt (LM) and the Particle Swarm Optimization (PSO) algorithms to devise the appropriate NN training weighting parameters, and then construct an effective Neural Network classifier to improve the Wisconsin Breast Cancers' classification accuracy and efficiency. Experimental results show that the accuracy and AROC improved emphatically, and the best performance in accuracy and AROC are 98.83% and 0.9971, respectively.

Although breast cancer, with easy recurrence and high mortality, has become one of the leading causes of cancer death in women, early and accurate diagnosis of breast cancer can effectively increase the likelihood of a cure. Therefore, it is particularly important to improve the accuracy of early diagnosis of breast cancer. However, conventional early diagnosis relies on human experience and has a low accuracy rate. Therefore, many researchers have proposed various machine learning methods to improve the accuracy and efficiency of prediction. Most of the existing studies around breast cancer classification adopt a single algorithm to fit breast cancer data but ignore the applicability of different breast cancer data features to the model. In this paper, we adopt machine algorithms to strip the features of machine learning methods from the rest of the features and attempt to enhance the model effect by designing deep learning model structures to find the hidden patterns in the rest of the features. In addition, due to strict medical data privacy requirements and high collection difficulty and cost, the model designed in this paper will be trained on a small number of samples. As a result, we attempt to find a minimization model for a breast cancer classification algorithm that features both low cost and high efficiency. At the same time, the deep learning model is further designed to complement the original model when it is possible to introduce complex data indicators. Experimental values show that the design model in this paper performs best not only under limited data and limited indicators but also under limited data complex indicators, demonstrating the effectiveness of the approach of mixed comparison and feature selection of multiple classification algorithms. In summary, the fusion model designed and implemented in this paper performs well in the experiments, and the accuracy of the model test reaches 98.3%.

This study focuses on the visitors of Pohon Cinta beach tourist area. This beach is one of the potential tourism objects in Pohuwato Regency. The main problem that frequently occurs is that many visitors cannot directly convey their impression when visiting and enjoying the beauty of the Pohon Cinta beach. The government needs to know the level of visitor satisfaction to attempt to improve and develop the Pohon Cinta beach tourist attraction. Thus, to solve the problem above, a method that can help predict visitor satisfaction is needed. This study aims to measure visitor satisfaction through predictions using the Backpropagation algorithm and PSO feature selection to assist the government in developing tourism potential in Pohuwato Regency. The method used is the backpropagation algorithm for prediction and Particle Swarm Optimization which is considered effective in overcoming optimization problems. This algorithm is considered capable of solving problems in the backpropagation algorithm. The accuracy value of the backpropagation algorithm model is 84.67%, the accuracy value of the PSO-based backpropagation algorithm model is 85.00%, and the difference in accuracy is 0.33. The results of the application of the Backpropagation algorithm and Particle Swarm Optimization can increase the predictive accuracy value of visitor satisfaction at the Cinta Tree Beach tourist attraction.

Tumor cell-released LC3+ extracellular vesicles (LC3+ EVs) participate in immunosuppression during autophagy and contribute to the occurrence and development of breast cancer. In view of the strong association between the LC3+ EVs and breast cancer, developing an effective strategy for the quantitative detection of LC3+ EVs levels with high sensitivity to identify LC3+ EVs as new biomarkers for accurate diagnosis of breast cancer is crucial, but yet not been reported. Herein, an ultrasensitive electrochemical immunosensor is presented for the quantitative determination of LC3+ EVs using a three-dimensional graphene oxide hydrogel-methylene blue composite as a redox probe, showing a low detection limit and a wide linear range. With this immunosensor, the expression levels of LC3+ EVs in various practical sample groups including different cancer cell lines, the peripheral blood of tumor-bearing mice before and after immunotherapy, and the peripheral blood from breast cancer patients with different subtypes and stages were clearly distinguished. This study demonstrated that LC3+ EVs were superior as biomarkers for the accurate diagnosis of breast cancer compared to traditional biomarkers, particularly for cancer subtype discrimination. This work would provide a new noninvasive detection tool for the early diagnosis and prognosis assessment of breast cancer in clinics.

Breast cancer is the most common cancer among women worldwide. Traditional approaches to breast cancer diagnosis and treatment are often one-size-fits-all, but precision oncology offers the potential to tailor treatment to the individual patient. AI-driven precision oncology can help to improve the accuracy of breast cancer diagnosis, predict the risk of recurrence, and develop personalized treatment plans. This paper presents an expert system for breast cancer diagnosis and personalized treatment that is based on AI. The expert system uses a variety of machine learning algorithms to analyze patient data, including clinical data, genomic data, and imaging data. The expert system can be used to diagnose breast cancer with high accuracy, predict the risk of recurrence and develop personalized treatment plans. The expert system has been evaluated on a large dataset of breast cancer patients. The results show that the expert system can achieve high accuracy in diagnosing breast cancer and predicting the risk of recurrence. The expert system can also be used to develop personalized treatment plans that are tailored to the individual patient.

Finding breast cancer early and accurately is very important for helping patients live longer. We use the Wisconsin Diagnostic Breast Cancer (WDBC) dataset to look at two popular machine learning classifiers: Support Vector Machines (SVM) and k-nearest neighbors (KNN). Our work combines theory with practice by including things like data preprocessing exploratory feature evaluation, model configuration, hyperparameter, and validation through cross-validation techniques. We use common evaluation metrics like accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) to judge performance. When we did experiments with both a 70:30 train-test split and 10-fold cross-validation, we found that SVM did a little better (~96.3% accuracy) than compared to KNN, which has an accuracy of about 96.5% and AUC values of about 0.96 and 0.968, respectively. SVM is better at dealing with high-dimensional data and making maximum-margin decision boundaries, but KNN is still appealing because it is simple and works well on datasets of moderate size. Even though both methods have their pros and cons, they also have some problems that are talked about, as well as ways they could be improved and how they could be used in the clinic. This paper evaluates and compares the theoretical and practical performance of SVM and KNN using the WDBC dataset. We include preprocessing, careful evaluation metrics, and hyperparameter optimization to provide an empirical comparison of both models. The study also discusses important trade-offs and real-world factors for using these algorithms in medical screening contexts.

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

The early and accurate diagnosis of breast cancer is of paramount importance in the symptomatic patient. In this study, the sensitivity and false negative rates of clinical assessment and community mammographic imaging are measured and compared. One hundred and sixty-nine histologically proven breast cancers, derived from one surgeon's practice, were clinically assessed. Of these, 108 were additionally assessed by mammography. Eighty-seven per cent of the patients with breast cancer presented with a lump. In 82.8% of cases the patient or her partner found the lump, while 12.3% of lumps were found by the family practitioner. The false negative rate for clinical diagnosis was 19%, and for mammographic diagnosis it was 29%. Premenopausal mammographic diagnosis had a significantly higher false negative rate (54%) than postmenopausal diagnosis (14%). Mammographic examination had a particularly high false negative rate (65%) when cancer was thought clinically to be of "low index of suspicion". The clinical evaluation of and the indications for biopsy of the symptomatic breast remain the essential step in the diagnosis of breast cancer.

Breast cancer is the second leading cause of death in women. It occurs when cells in the breast start to grow out of proportion and invade neighboring tissues or spread throughout the body. Mammography is one of the most effective and popular modalities presently used for breast cancer screening and detection. Efforts have been made to improve the accuracy of breast cancer diagnosis using different imaging modalities. Ultrasound and magnetic resonance imaging have been used to detect breast cancers in high risk patients. Recently, electrical impedance imaging and nuclear medicine techniques are also being widely used for breast cancer screening and diagnosis. In this paper, we discuss the capabilities of various breast imaging modalities.

Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical datasets include a large variety of data that can lead to confusion during diagnosis. The Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires elimination of inappropriate and repeated data from the dataset before final diagnosis. This can be done using any of the feature selection algorithms available in data mining. Feature selection is considered as a vital step to increase the classification accuracy. This paper proposes a Modified Bat Algorithm (MBA) for feature selection to eliminate irrelevant features from an original dataset. The Bat algorithm was modified using simple random sampling to select the random instances from the dataset. Ranking was with the global best features to recognize the predominant features available in the dataset. The selected features are used to train a Random Forest (RF) classification algorithm. The MBA feature selection algorithm enhanced the classification accuracy of RF in identifying the occurrence of breast cancer. The Wisconsin Diagnosis Breast Cancer Dataset (WDBC) was used for estimating the performance analysis of the proposed MBA feature selection algorithm. The proposed algorithm achieved better performance in terms of Kappa statistic, Mathew’s Correlation Coefficient, Precision, F-measure, Recall, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE).

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. In this paper, a swarm intelligence technique based support vector machine classifier (PSO_SVM) is proposed for breast cancer diagnosis. In the proposed PSO-SVM, the issue of model selection and feature selection in SVM is simultaneously solved under particle swarm (PSO optimization) framework. A weighted function is adopted to design the objective function of PSO, which takes into account the average accuracy rates of SVM (ACC), the number of support vectors (SVs) and the selected features simultaneously. Furthermore, time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search in PSO algorithm. The effectiveness of PSO-SVM has been rigorously evaluated against the Wisconsin Breast Cancer Dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The proposed system is compared with the grid search method with feature selection by F-score. The experimental results demonstrate that the proposed approach not only obtains much more appropriate model parameters and discriminative feature subset, but also needs smaller set of SVs for training, giving high predictive accuracy. In addition, Compared to the existing methods in previous studies, the proposed system can also be regarded as a promising success with the excellent classification accuracy of 99.3% via 10-fold cross validation (CV) analysis. Moreover, a combination of five informative features is identified, which might provide important insights to the nature of the breast cancer disease and give an important clue for the physicians to take a closer attention. We believe the promising result can ensure that the physicians make very accurate diagnostic decision in clinical breast cancer diagnosis.

<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>

Introduction: Breast cancer is the second cause of mortality among women. Early detection is the only rescue to reduce the risk of breast cancer mortality. Traditional methods cannot effectively diagnose tumor since they are based on the assumption of well-balanced dataset.. However, a hybrid method can help to alleviate the two-class imbalance problem existing in the diagnosis of breast cancer and establish a more accurate diagnosis. Material and Methods: The proposed hybrid approach was based on improved Laplacian score (LS) andK-nearest neighbor (KNN) algorithms called LS-KNN. An improved LS algorithm was used for obtaining the optimal feature subset. The KNN with automatic K was utilized for classifying the data which guaranteed the effectiveness of the proposed method by reducing the computational effort and making the classification more faster. The effectiveness of LS-KNN was also examined on two biased-representative breast cancer datasets using classification accuracy, sensitivity, specificity, G-mean, and Matthews correlation coefficient. Results: Applying the proposed algorithm on two breast cancer datasets indicated that the efficiency of the new method was higher than the previously introduced methods. The obtained values of accuracy, sensitivity, specificity, G-mean, and Matthews correlation coefficient were 99.27%, 99.12%, 99.51%, 99.42%, respectively. Conclusion: Experimental results showed that the proposed approach worked well with breast cancer datasets and could be a good alternative to the well-known machine learning methods

This study aims to improve the accuracy of breast cancer diagnosis by constructing a binary classification model through machine learning-based extraction of radiomic features from breast ultrasound images. A total of 780 breast ultrasound images from 600 female patients aged 2575 years were selected and divided into "diseased" and "non-diseased" groups. Features including first-order statistics, morphological characteristics, texture parameters, and a self-created concentric grey-level fitting curve slope feature were extracted. Six classifiers, including SVM and KNN, were used to construct models, which were evaluated using ten-fold stratified cross-validation. Results showed that model performance improved across all approaches when incorporating the self-created feature. Notably, the LightGBM model exhibited enhanced discriminatory capability, with AUC increasing from 0.683 to 0.715. This indicates that machine learning-based radiomics feature extraction can effectively support breast cancer diagnosis.