Diabetes prediction using ensemble perceptron algorithm

Today, people's new way of life leads their eating habits towards fast-foods and ready-to-use products more than before. These foods contain large amounts of sugar and fat, which increase the number of people at risk of diabetes. Many people are required to get diabetes diagnosis by various blood tests regularly. These tests bring significant amounts of cost and take facilities and time when it comes to a large number of people. Machine learning algorithms can be used as computer aided systems to predict if a person is highly probable to have diabetes or not, in order to reduce huge number of people who require to take diagnosis blood tests, to save time and money. In this study, we proposed a learning algorithm which ensemble boosting algorithm with perceptron algorithm to improve performance of perceptron algorithm in prediction of undiagnosed patients. Proposed method is tested on three different publicly available datasets and compared with performance of perceptron algorithm. The results show that proposed algorithm outperform perceptron algorithm on average AUC basis.