Blended Ensemble Learning Prediction Model for Strengthening Diagnosis and Treatment of Chronic Diabetes Disease

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 9
• Main Authors: Mahesh, T. R., Kumar, Dhilip, Vinoth Kumar, V., Asghar, Junaid, Mekcha Bazezew, Banchigize, Natarajan, Rajesh, Vivek, V.
• Format: Journal Article
• Language: English
• Published: New York Hindawi 14.07.2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Accuracy; Algorithms; Bayesian analysis; Blood; Blood sugar; Care and treatment; Classifiers; Datasets; Decision trees; Diabetes; Diabetes mellitus; Diabetes therapy; Diagnosis; Disease; Ensemble learning; Glucose; Hunger; Hyperglycemia; Illnesses; Insulin; Insulin resistance; Machine learning; Medical research; Medicine, Experimental; Neural networks; Pancreas; Prediction models; Pregnancy; Radial basis function; Support vector machines; Thirst; Type 2 diabetes; Urination; Womens health
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/4451792

Diabetes mellitus (DM), commonly known as diabetes, is a collection of metabolic illnesses characterized by persistently high blood sugar levels. The signs of elevated blood sugar include increased hunger, frequent urination, and increased thirst. If DM is not treated properly, it may lead to several complications. Diabetes is caused by either insufficient insulin production by the pancreas or an insufficient insulin response by the body’s cells. Every year, 1.6 million individuals die from this disease. The objective of this research work is to use relevant features to construct a blended ensemble learning (EL)-based forecasting system and find the optimal classifier for comparing clinical outputs. The EL based on Bayesian networks and radial basis functions has been proposed in this article. The performances of five machine learning (ML) techniques, namely, logistic regression (LR), decision tree (DT) classifier, support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF), are compared with the proposed EL technique. Experiments reveal that the EL method performs better than the existing ML approaches in predicting diabetic illness, with the remarkable accuracy of 97.11%. The proposed ensemble learning could be useful in assisting specialists in accurately diagnosing diabetes and assisting patients in receiving the appropriate therapy.