Breast cancer is one of the most common invasive cancers in women and it continues to be a worldwide medical problem since the number of cases has significantly increased over the past decade. Breast cancer is the second leading cause of death from cancer in women. The early detection of breast cancer can save human life but the traditional approach for detecting breast cancer disease needs various laboratory tests involving medical experts. To reduce human error and speed up breast cancer detection, an automatic system is required that would perform the diagnosis accurately and timely. Despite the research efforts for automated systems for cancer detection, a wide gap exists between the desired and provided accuracy of current approaches. To overcome this issue, this research proposes an approach for breast cancer prediction by selecting the best fine needle aspiration features. To enhance the prediction accuracy, several feature selection techniques are applied to analyze their efficacy, such as principal component analysis, singular vector decomposition, and chi-square (Chi2). Extensive experiments are performed with different features and different set sizes of features to investigate the optimal feature set. Additionally, the influence of imbalanced and balanced data using the SMOTE approach is investigated. Six classifiers including random forest, support vector machine, gradient boosting machine, logistic regression, multilayer perceptron, and K-nearest neighbors (KNN) are tuned to achieve increased classification accuracy. Results indicate that KNN outperforms all other classifiers on the used dataset with 20 features using SVD and with the 15 most important features using a PCA with a 100% accuracy score.

Artificial intelligence (AI) comprises various sub-fields, including machine learning (ML) and deep learning (DL) perform a key role in the transformation of many industries, including education. It changes traditional learning methods by using its Innovative techniques and applications. Using its applications, the teachers may keep track of each student's development, paying close attention to the areas in which they struggle. Many researchers are working with ML and DL to exploit its discoveries and insights. In education, traditional education methods (TEM) are the same for each student, which means each student is taught in the same way as ML and DL, making this process flexible and creative for solving complex problems and enhancing productivity. Nowadays, each institution adopts E-learning methods as the primary way of learning, especially during the pandemic. Despite this evolution of creativity, delivering quality education, making strategies for analyzing performance and future goals, and career counseling for students still pose challenges. The current study aims to offer a complete overview of the significance of ML approaches in online education. To accomplish this purpose, the study synthesizes information from multiple scientific papers that investigate (a) the methodology used to construct learning analysis tools, (b) the key data resources used, and (c) the scope of data sources now available. This systematic literature review (SLR) examines the research conducted between 1961 and 2022, focusing on various machine learning (ML) and deep learning (DL) techniques. Its aim is to provide insights into the applications of these techniques and offer optimal solutions to the research questions at hand. We are convinced that our complete assessment will be a dependable resource for the research group in ascertainment the best approach and information source for their unique needs. Moreover, our findings provide valuable insights on the subject matter that could aid the research community in their future endeavors in the related field.