Predictive modelling and analytics for diabetes using a machine learning approach. Issue 1 (28th July 2020)

• Record Type: Journal Article
• Authors: Kaur, Harleen; Kumari, Vinita
• Is Part Of: Applied computing and informatics. Volume 18:Issue 1/2(2022)
• Page Start: 90
• Page End: 100
• Publication Date: 2020-07-28
• Subjects: Machine learning -- Support vector machine (SVM) -- k-Nearest neighbour (k-NN) -- Artificial neural network (ANN) -- Multifactor dimensionality reduction (MDR); Information science -- Periodicals; Information storage and retrieval systems -- Periodicals; 004
• DOI: 10.1016/j.aci.2018.12.004
• Languages: English
• ISSNs: 2210-8327

Abstract

Abstract : Diabetes is a major metabolic disorder which can affect entire body system adversely. Undiagnosed diabetes can increase the risk of cardiac stroke, diabetic nephropathy and other disorders. All over the world millions of people are affected by this disease. Early detection of diabetes is very important to maintain a healthy life. This disease is a reason of global concern as the cases of diabetes are rising rapidly. Machine learning (ML) is a computational method for automatic learning from experience and improves the performance to make more accurate predictions. In the current research we have utilized machine learning technique in Pima Indian diabetes dataset to develop trends and detect patterns with risk factors using R data manipulation tool. To classify the patients into diabetic and non-diabetic we have developed and analyzed five different predictive models using R data manipulation tool. For this purpose we used supervised machine learning algorithms namely linear kernel support vector machine (SVM-linear), radial basis function (RBF) kernel support vector machine, k -nearest neighbour ( k -NN), artificial neural network (ANN) and multifactor dimensionality reduction (MDR).