A novel multistage ensemble approach for prediction and classification of diabetes - Simaiya, Sarita; Kaur, Rajwinder; Sandhu, Jasminder Kaur; Alsafyani, Majed; Alroobaea, Roobaea; alsekait, Deema mohammed; Margala, Martin; Chakrabarti, Prasun (Frontiers in Physiology, 2022)
Diabetes mellitus is a metabolic syndrome that affects millions of people across the world. Every year, the rate of occurrence rises drastically. Diabetes-related problems across several vital organs of the body can be fatal if left untreated. Diabetes must be detected early to receive proper treatment, preventing the condition from escalating to severe problems. Tremendous advancements in health sciences and biotechnology have resulted in massive data that generated massive Electronic Health Records and clinical information. The exponential increase of electronically gathered information has resulted in more complicated, accurate prediction models that can be updated continuously using machine learning techniques. This research mainly emphasizes discovering the best ensemble model for predicting diabetes. A new multistage ensemble model is proposed for diabetes prediction. In this model, accuracy is predicated on the Pima Indian Diabetes dataset. The accuracy of the proposed ensemble model is compared with the existing machine learning model, and the experimental results demonstrate the performance of the proposed model in terms of higher Precision, f-measure, Recall, and AUC.