Polycystic ovary syndrome (PCOS) has been classified as a severe health problem common among women globally. Early detection and treatment of PCOS reduce the possibility of long-term complications, such as increasing the chances of developing type 2 diabetes and gestational diabetes. Therefore, effective and early PCOS diagnosis will help the healthcare systems to reduce the disease’s problems and complications. Machine learning (ML) and ensemble learning have recently shown promising results in medical diagnostics. The main goal of our research is to provide model explanations to ensure efficiency, effectiveness, and trust in the developed model through local and global explanations. Feature selection methods with different types of ML models (logistic regression (LR), random forest (RF), decision tree (DT), naive Bayes (NB), support vector machine (SVM), k-nearest neighbor (KNN), xgboost, and Adaboost algorithm to get optimal feature selection and best model. Stacking ML models that combine the best base ML models with meta-learner are proposed to improve performance. Bayesian optimization is used to optimize ML models. Combining SMOTE (Synthetic Minority Oversampling Techniques) and ENN (Edited Nearest Neighbour) solves the class imbalance. The experimental results were made using a benchmark PCOS dataset with two ratios splitting 70:30 and 80:20. The result showed that the Stacking ML with REF feature selection recorded the highest accuracy at 100 compared to other models.

In recent years, service-oriented based Internet of Things (IoT) has received massive attention from research and industry. Integrating and composing smart objects functionalities or their services is required to create and promote more complex IoT applications with advanced features. When many smart objects are deployed, how do we select the most appropriate set of smart objects to compose a service by considering both energy and Quality of Service (QoS) is an essential and challenging task. In this work, we reduced the problem of finding an optimal balance between QoS level and the consumed energy of the IoT service composition to a Bi-objective Shortest Path Optimization (BSPO) problem and used an exact algorithm named pulse to solve the problem. The BSPO has two objectives, minimizing the QoS including execution time, network latency, and service price and minimize the energy consumption of the composite service. Experimental evaluations show that the proposed approach has short execution time in various complex service profiles. Meanwhile, it can obtain good performance in energy consumption and thus network lifetime while maintaining a reasonable QoS level.