Diabetes detection based on Fast Learning Network model

In the last decades, machine learning algorithms have witnessed a high significance in healthcare applications in terms of detecting various diseases. For instance, diabetic disease is considered one of the major health problems around the world. However, there is a need to deeply study a machine learning algorithm in the detection of diabetic disease. Therefore, this study presents a Fast Learning Network (FLN) algorithm in the detection of diabetic disease based on different numbers of hidden nodes. In this work, the Pima Indians Diabetes Database (PIDD) is used for training and testing the proposed FLN algorithm. Furthermore, the performance of the proposed model has been assessed in terms of several evaluation measurements such as accuracy, precision, recall, F-Measure, G-Mean, MCC, and specificity. The experimental results show that the highest achieved accuracy, recall, F-Measure, G-Mean, and MCC were 82.17%, 80.95%, 71.33%, 71.84%, and 59.54%, respectively. Meanwhile, the highest obtained results for precision and specificity were 67.50% and 83.12%, respectively. In addition, the performance of the proposed model has outperformed its comparative in terms of detection accuracy.