Diabetic kidney disease (DKD) is a diabetic microvascular complication often characterized by an unpredictable progression. Hence, early detection and recognition of patients vulnerable to progression is crucial. To develop a prediction model to identify the stages of DKD and the factors contributing to progression to each stage using machine learning. A retrospective study was conducted in a South Indian tertiary care hospital and collected the details of patients diagnosed with DKD from January 2017 to January 2022. Bayesian optimization-based machine learning techniques such as classification and regression were employed. The model was developed with the help of an optimization framework that effectively balances classification, prediction accuracy, and explainability. Of the 311 patients diagnosed with DKD, 227 were selected for the study. A system for predicting DKD has been created for a patient dataset utilizing a variety of machine-learning approaches. The eXtreme gradient (XG) Boost method excelled, achieving 88.75% accuracy, 88.57% precision, 91.4% sensitivity,100% specificity, and 89.49% F1-score. An interpretable data-driven method highlights significant features for early DKD diagnosis. The best explainable prediction model uses the XG Boost classifier, revealing serum uric acid, urea, phosphorous, red blood cells, calcium, and absolute eosinophil count as the major predictors influencing the progression of DKD. In the case of regression models, the gradient boost regressor performed the best, with an R² score of 0.97. Machine learning algorithms can effectively predict the stages of DKD and thus help physicians in providing patients with personalized care at the right time.

Breast cancer (BC) is a leading cause of mortality in women worldwide, and its incidence and prognosis vary greatly, especially in low-income areas such as Asia and Africa, compared to America and Europe. This review examines the critical role of computer-aided diagnostic systems utilizing Deep Learning (DL) techniques in improving the precision of BC detection, which can help researchers and practitioners better understand the obstacles and emerging trends in the field. The comprehensive analysis and synthesis of the published articles is provided by the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA). Following several searches, 287 articles were found eligible for the assessment followed by data extraction and synthesis. This work provides insight into the pathology of BC and its corresponding mammographic appearance, a comprehensive analysis of publication trends, important contributors, and emerging themes using bibliometric techniques in addition to a thorough review of research articles. Focus maps were also created to identify and shed light on the body of knowledge. In contrast to other research, this review sheds light on the subtle identification of breast density, mass, and calcification from mammography images by emphasizing the critical evaluation of image pre- processing, augmentation, detection, segmentation, and classification techniques used from 2018 to 2023, further highlighting articles related to the Asian dataset. Furthermore, the review examines contributions made by authors, countries, and experimental datasets, providing insights into various approaches. Prospero registration: CRD42022478896