Summary Blueberries, a functional food, are rich in bioactive polyphenols and anthocyanins. However, the shelf life is short and requires cold storage. This study provides evidence that edible foxtail millet flour (FMF) efficiently sorbs only blueberry bioactive components (polyphenols and anthocyanins) but not sugars, improves their stability and retains the activity. The concentration of blueberry polyphenols and anthocyanins sorbed to FMF ranged from 6 to 113 and 4 to 41 mg g−1, respectively. The concentration of bioactive components in one serving of blueberries (73 g) is equivalent to those present in 1.2 g of blueberry‐enriched foxtail millet flour (BFMF). The blueberry bioactive sorbed onto FMF remained stable for at least 16 weeks at 40 °C. BFMF eluates inhibited α‐glucosidase enzyme activity and scavenged the free radicals conferring that blueberry bioactive components in BFMF retained the activity. The sorption process described here provides a practical way of creating low glycemic protein‐rich edible flour enriched with plant bioactive compounds without sugars.

Systematic Lupus Erythematosus (SLE) is an irreversible autoimmune disease that has seen to bring a lot of negative effect on the human body. It has become a very challenging task in predicting the prevalence of Lupus in patients. It has slowly gained popularity among many researchers to study the prevalence of this disease and developing prediction models that not only study the prevalence of the disease but is also able to predict suitable dosage requirements, treatment effectiveness and the severity of the disease in patients. All of these is usually done with medical records or clinical data that has different attributes related and significant to the analysis done. With the advancement in machine learning models and ensemble techniques, accurate prediction models have been developed. However, these models are not able to explain the significant contributing factors as well as correctly classify the severity of the disease. Decision Tree Classifier, Random Forest Classifier and Extreme Gradient Boosting (XGBoost) are the models that will be used in this paper to predict the early prevalence to Lupus Disease in patients using clinical records. The most significant factors affecting Systematic Lupus Erythematosus (SLE) will then be identified to aid medical practitioners to take suitable preventive measures that can manage the complications that arise from the disease. Hence, this paper aims to assess the performance of tree models by performing several experiments on the hyper parameters to develop a more accurate model that is able to classify Lupus Disease in patients in the early stages. Findings revealed that the best model was the Random Forest Classifier with parameter tuning. The most significant factor that affected the presence of Lupus Disease in patients was identified as the Ethnicity and the Renal Outcome or the kidney function of the patients.