An Improved Artificial Neural Network Model for Effective Diabetes Prediction

TLDR

Data analytics and artificial neural networks have been widely applied in healthcare for disease diagnosis, image processing, decision support, and prediction. The study aims to determine how input parameters influence diabetes prediction to classify patients as diabetic or not. An improved ANN using backpropagation scaled conjugate gradient was trained on the Pima Indian Diabetes dataset, testing hidden layer sizes from 5 to 50 and evaluating accuracy and mean squared error. The ABP‑SCGNN model with 20 hidden neurons achieved 93 % accuracy, outperforming other ANN variants and demonstrating its effectiveness for diabetes prediction.

Abstract

Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks (ANNs) in the medical discipline emerged for data‐driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system (DSS), and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network (ABP‐SCGNN) algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes (PID) dataset using accuracy and mean squared error (MSE) as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP‐SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes.