Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms

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%. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.