Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms

• Published in: SN computer science Vol. 4; no. 1; p. 72
• Main Authors: Theerthagiri, Prasannavenkatesan, Ruby, A. Usha, Vidya, J.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.01.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Classification; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Datasets; Decision making; Decision trees; Diabetes; Diabetes mellitus; Diagnosis; Discriminant analysis; Disease; Glucose; Information Systems and Communication Service; Insulin resistance; Machine learning; Multilayer perceptrons; Neural networks; Original Research; Pattern Recognition and Graphics; Radial basis function; Regression analysis; Software Engineering/Programming and Operating Systems; Support vector machines; Vision; MLP; Classification of diabetes; Diabetes prediction; Machine learning algorithm
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01485-3

Diabetes mellitus is characterized as a chronic disease that may cause many complications. Machine learning algorithms are used to diagnose and predict diabetes. The learning-based algorithms play a vital role in supporting decision-making in disease diagnosis and prediction. This paper investigates traditional classification algorithms and neural network-based machine learning for the diabetes dataset. Also, various performance methods with different aspects are evaluated for the K-nearest neighbor, Naive Bayes, extra trees, decision trees, radial basis function, and multilayer perceptron algorithms. It supports the estimation of patients who possibly suffer from diabetes in the future. This work shows that the multilayer perceptron algorithm gives the highest prediction accuracy with the lowest MSE of 0.19. The MLP gives the lowest false-positive and false-negative rates with the highest area under the curve of 86%.