DeepMed: A Multi-Model Deep Learning Algorithm for Predicting Chronic Diseases
TL;DR
A novel multi-model approach was used in which ten additional neural networks were trained separately and used to predict the conditions through bootstrapping and bagging techniques, and could potentially aid in enabling early disease prediction, facilitating timely treatment, and improving quality of life.
Abstract
Prognostic modelling is an essential tool in clinical sciences. A machine learning approach can efficiently help with disease prediction and prevention by integrating large amounts of structured patient records. This study develops a deep learning framework to assist in early disease detection and clinical decision-making. A hospital patient dataset was used to develop a predictive model for 13 chronic conditions. The dataset was preprocessed, including the use of a denoising autoencoder neural network for imputations. To improve the predictive ability of the model and reduce overfitting, a novel multi-model approach was used in which ten additional neural networks were trained separately and used to predict the conditions through bootstrapping and bagging techniques. The program was able to predict the 13 conditions with an average accuracy of 96.95% and a micro-averaged F1 score of 0.91 on an independent test set, with the F1 score calculated to account for the presence of class imbalance. This program can potentially aid in enabling early disease prediction, facilitating timely treatment, and improving quality of life while lowering treatment costs and helping hospitals, emergency rooms, caregivers, and individuals with diagnosis predictions, prompt decision-making, and resource prioritization, especially in times of limited healthcare resources.