The rapid population growth results in a crucial problem in the early detection of diseases in medical research. Among all the cancers unveiled, breast cancer is considered the second most severe cancer. Consequently, an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses. Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective, reliable, and rapid responses, which could help in decreasing the death risk. In this paper, we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers, namely, guided whale and dipper throated optimizers. The proposed algorithm is evaluated using four publicly available breast cancer datasets. The evaluation results show the effectiveness of the proposed approach from the accuracy and speed perspectives. To prove the superiority of the proposed algorithm, a set of competing feature selection algorithms were incorporated into the conducted experiments. In addition, a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed algorithm. The best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%. This result is achieved in an average time of 3.6725 s, the best result among all the competing approaches utilized in the experiments.

Environmental transition can potentially influence cardiovascular health. Investigating the relationship between such transition and heart disease has important applications. This study uses federated learning (FL) in this context and investigates the link between climate change and heart disease. The dataset containing environmental, meteorological, and health-related factors like blood sugar, cholesterol, maximum heart rate, fasting ECG, etc., is used with machine learning models to identify hidden patterns and relationships. Algorithms such as federated learning, XGBoost, random forest, support vector classifier, extra tree classifier, k-nearest neighbor, and logistic regression are used. A framework for diagnosing heart disease is designed using FL along with other models. Experiments involve discriminating healthy subjects from those who are heart patients and obtain an accuracy of 94.03%. The proposed FL-based framework proves to be superior to existing techniques in terms of usability, dependability, and accuracy. This study paves the way for screening people for early heart disease detection and continuous monitoring in telemedicine and remote care. Personalized treatment can also be planned with customized therapies.