Computational approach to clinical diagnosis of diabetes disease: a comparative study

• Published in: Multimedia tools and applications Vol. 80; no. 20; pp. 30091 - 30116
• Main Authors: Gupta, Deepak, Choudhury, Ambika, Gupta, Umesh, Singh, Priyanka, Prasad, Mukesh
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2021; Springer Nature B.V
• Subjects: 1155T: Advanced machine learning algorithms for biomedical data and imaging; Classifiers; Comparative studies; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Datasets; Decision trees; Diabetes; Diagnosis; Discriminant analysis; Gaussian process; Machine learning; Metabolic disorders; Multimedia Information Systems; Regression; Regression analysis; Special Purpose and Application-Based Systems; Statistical analysis; Support vector machines; Support vector machines; K-nearest neighbors; Logistic regression; Diabetes; Machine learning; ELM
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-10242-8

Diabetes is one of the most prevalent non-communicable diseases and is the 6th leading cause of death worldwide. It’s a chronic metabolic disorder which has no cure, however, it is a highly treatable condition, if diagnosed and managed on time to avoid its complications. This paper explores and compares various machine learning (ML) approaches that can help in determining the risk of diabetes at an early stage and aid in improving the medical diagnosis of diabetes. The paper considers two real-world datasets one is a diabetic clinical dataset (DCA) collected from a medical practitioner in the state of Assam, India during the year 2017–2018 and other is public PIMA Indian diabetic dataset. To analyze the various machine learning techniques on DCA and PIMA Indian diabetic datasets for the classification of diabetic and non-diabetic patients, different classifiers like perceptron, Gaussian process, linear discriminant analysis, quadratic discriminant analysis, statistical gradient descent, ridge regression classifier, support vector machines, k-nearest neighbors, decision tree, naïve Bayes, logistic regression, random forest and ELM for multiquadric, RBF, sigmoid activation functions are used. The results of numerical experiments suggested that logistic regression yields better performance in comparison to the other techniques.