A semi-supervised learning based method: Laplacian support vector machine used in diabetes disease diagnosis

Abstract

Pattern recognition methods could be of great help to disease diagnosis. In this study, a semi-supervised learning based method, Laplacian support vector machine (LapSVM), was used in diabetes diseases prediction. The diabetes disease dataset used in this article is Pima Indians diabetes dataset obtained from the UCI Repository of Machine Learning Databases and all patients in the dataset are females at least 21 years old of Pima Indian heritage. Firstly, LapSVM was trained as a fully-supervised learning classifier to predict diabetes dataset and 79.17% accuracy was obtained. Then, it was trained as a semi-supervised learning classifier and we got the prediction accuracy 82.29%. The obtained accuracy 82.29% is higher than other previous reports. The experiments led to the finding that LapSVM offers a very promising application, i.e., LapSVM can be used to solve a fully-supervised learning problem by solving a semi-supervised learning problem. The result suggests that LapSVM can be of great help to physicians in the process of diagnosing diabetes disease and it could be a very promising method in the situations where a lot of data are not class-labeled.