Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy

The initial identification of breast cancer and the prediction of its category have become a requirement in cancer research because they can simplify the subsequent clinical management of patients. The application of artificial intelligence techniques (e.g., machine learning and deep learning) in medical science is becoming increasingly important for intelligently transforming all available information into valuable knowledge. Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. In addition, the ranked order of the variables based on their importance differed across the ML algorithms. The results demonstrated that the three bagging ensemble ML algorithms, namely, RF ERT and DT, yielded better classification accuracies (78–86%) compared with the two boosting algorithms, GBT and ADB (60–75%). We hope that these our results would help improve the classification of breast tissue to allow the early prediction of cancer susceptibility

Detection of Breast Cancer using Deep Learning Techniques

Because of the current population boom in health research, early sickness diagnosis has become a vital concern. As the population expands, the risk of dying from breast cancer rises dramatically. Breast carcinoma has been identified whenever the second most dangerous of the previously described malignancies. The researcher automated illness detection system assists medical practitioners in disease diagnosis, provides consistent, effective, and punctual intervention, and reduces the risk of death. Any disease that is diagnosed appropriately and promptly may be treated with minimal human intervention. An overwhelming majority of people are unaware of their illness until it becomes chronic. It increases the world mortality rate. Breast carcinoma has emerged as one of the increasingly rare diseases that may be treated if detected early enough and before it spreads to other regions of the body. Breast carcinoma constitutes one of the most frequent malignancies in women globally, and early identification is critical for improving survival and treatment success. Breast cancer detection technologies in areas like mammography and ultrasound have limits outside in the sense of preciseness as well as sensitivity. Deep learning algorithms have begun to emerge as a potential strategy for enhancing the degree of certainty and efficiency belonging to breast cancer diagnosis in recent years. Deep learning is an artificial intelligence area that focuses down training multi-layer neural networks to gain knowledge of and extract complicated patterns from big datasets. Researchers have developed sophisticated models suited to successfully diagnosing breast cancer from several medical imaging modalities, which might involve mammograms, MRI scans, additionally histopathological images, by utilizing the power throughout deep learning algorithms. Breast carcinoma detection is an important subject of study with significant public health implications. Deep learning techniques, a subset of computational neuroscience (AI), demonstrate excellent results in identifying and identifying cases of breast cancer. Deep learning breast cancer detection technologies have significant research repercussions due to the fact that they enable early diagnosis, enhance exactness, automate screening processes, give personalized treatment, and together with expand healthcare services to underserved areas. Persevered research in this area has the potential to change breast cancer diagnostics, resulting in better patient outcomes and, eventually, lifesaving. In this research we will be using The Weighted Product Model. The Weighted Product Model (WPM) represents a decision-making approach that uses numerous criteria to evaluate and rank options. It applies a multiple-criteria analysis approach that considers the value or weight assigned to every criterion as well as the effectiveness or score residing in every possible outcome on those criteria. Taken of Alternative Parameters SVM, Random Forest, Logistic Regression, KNN, Naive Bayes.  Taken of Evaluation Parameters Accuracy, Recall, Precision, FI-Score, ROC AUC. As per Weighted Normalized Decision Matrix we get to know that SVM got more value were Random Forest, Logistic Regression, Naive Bayes got less value.  From the above results I conclude that as per Weighted Normalized Decision Matrix we get to know that SVM got more value than others