Diabetes, especially T2DM, continues to be a significant health problem. One of the major concerns associated with diabetes is the development of its complications. Diabetic nephropathy, one of the chronic complication of diabetes, adversely affects the kidneys, leading to kidney damage. Diagnosing diabetic nephropathy involves considering various criteria, one of which is the presence of a pathologically significant quantity of albumin in urine, known as albuminuria. Thus, early prediction of albuminuria in diabetic patients holds the potential for timely preventive measures. This study aimed to develop a supervised learning model to predict the risk of developing albuminuria in T2DM patients. The selected supervised learning algorithms included Na\"ive Bayes, Support Vector Machine (SVM), decision tree, random forest, AdaBoost, XGBoost, and Multi-Layer Perceptron (MLP). Our private dataset, comprising 184 entries of diabetes complications risk factors, was used to train the algorithms. It consisted of 10 attributes as features and 1 attribute as the target (albuminuria). Upon conducting the experiments, the MLP demonstrated superior performance compared to the other algorithms. It achieved accuracy and f1-score values as high as 0.74 and 0.75, respectively, making it suitable for screening purposes in predicting albuminuria in T2DM. Nonetheless, further studies are warranted to enhance the model's performance.

Artificial intelligence is constantly being used in new and different applications in healthcare. A research team from the University of Gothenburg, is now using the power of AI in combination with conventional statistical methods in a study of risk factors in type 1 diabetes. The study's objective was to identify the most important indicators of elevated risk for cardiovascular disease and death. "What's unique about this study is that we've included machine learning analyses - that is, algorithms for AI - to assess strength of association for cardiovascular risk factors," Aidin Rawshani, PhD, of Sahlgrenska Academy, University of Gothenburg, said in a release. Dr. Rawshani is the corresponding author of a new article in the journal Circulation.