Chronic diseases, a global public health challenge, necessitate the deployment of cutting-edge predictive models for early diagnosis and personalized interventions. This study presents an advanced methodology for early prediction of chronic diseases, including heart attack, diabetes, breast cancer, and kidney disease, leveraging a synergistic combination of cutting-edge techniques. Recognizing the challenge posed by extensive medical datasets with numerous features, we introduce a novel approach that begins with Feature Engineering using Recursive Feature Elimination (RFE) in conjunction with a Support Vector Machine (SVM). The presented methodology identifies and removes irrelevant features to simplify data complexity. The refined dataset is then input into the robust eXtreme Gradient Boosting (XGBoost) classifier, known for its efficiency and adeptness in predicting complex relationships within the data. The chosen ensemble learning algorithm demonstrates significant prowess in inducing intricate patterns crucial for chronic disease prediction. To enhance model performance, an essential phase of optimization is introduced through hyperparameter tuning using Bayesian optimization. This strategically navigates the hyperparameter space, maximizing the efficiency of the search process and fine-tuning the model for optimal predictive accuracy. The proposed approach showcases a substantial improvement in the early prediction of chronic diseases, demonstrating the effectiveness of the proposed approach.

Software quality is one of the most important factors for assessing the global competitive position of any software company. Thus, the quantification of the quality parameters and integrating them into the quality models is very essential.Many attempts have been made to precisely quantify the software quality parameters using various models such as Boehm's Model, McCall's Model and ISO/IEC 9126 Quality Model. A major challenge, although, is that effective quality models should consider two types of knowledge: imprecise linguistic knowledge from the experts and precise numerical knowledge from historical data.Incorporating the experts’ knowledge poses a constraint on the quality model; the model has to be transparent.In this study, the authorspropose a process for developing fuzzy logic‐based transparent quality prediction models.They applied the process to a case study where Mamdani fuzzy inference engine is used to predict software maintainability.Theycompared the Mamdani‐based model with other machine learning approaches.The resultsshow that the Mamdani‐based model is superior to all.