A Novel Stacking-Based Deterministic Ensemble Model for Infectious Disease Prediction

Abstract

Infectious Disease Prediction aims to anticipate the aspects of both seasonal epidemics and future pandemics. However, a single model will most likely not capture all the dataset’s patterns and qualities. Ensemble learning combines multiple models to obtain a single prediction that uses the qualities of each model. This study aims to develop a stacked ensemble model to accurately predict the future occurrences of infectious diseases viewed at some point in time as epidemics, namely, dengue, influenza, and tuberculosis. The main objective is to enhance the prediction performance of the proposed model by reducing prediction errors. Autoregressive integrated moving average, exponential smoothing, and neural network autoregression are applied to the disease dataset individually. The gradient boosting model combines the regress values of the above three statistical models to obtain an ensemble model. The results conclude that the forecasting precision of the proposed stacked ensemble model is better than that of the standard gradient boosting model. The ensemble model reduces the prediction errors, root-mean-square error, for the dengue, influenza, and tuberculosis dataset by approximately 30%, 24%, and 25%, respectively.

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

“…They also include a convolutional autoencoder and transfer learning-based scheme for Alzheimer’s disease visualization [ 53 ]; a perceptron neural network for bacterial behavior programming [ 54 ]; and a deep neural architecture with generative adversarial network for brain tumor classification [ 55 ]. In addition, they include a deep neural network for epidemic prediction of COVID disease [ 56 ]; deep learning for sequential analysis of biomolecules [ 57 ]; elastic net and neural networks for the identification of plant genomics [ 58 ]; data mining and machine learning algorithms based on spectral clustering, random forest, and neural networks for cancer diagnosis through gene data [ 8 ]; and a stacking ensemble model based on an auto-regressive integrated moving average, exponential smoothing, a neural network autoregressive, a gradient-boosting regression tree, and extreme gradient boost models for infectious diseases [ 9 ]. Finally, there are supervised machine learning algorithms for lung disease detection, respiratory sound analyses, and so on [ 10 ].…”

“…For instance, in [ 7 ], Anninou et al developed a mathematical model for PDI by exploiting the concept of fuzzy cognitive maps, and a generic algorithm was proposed based on nonlinear Hebbian learning techniques. Recently, a wide use of artificial intelligence methodologies for modeling different diseases has emerged [ 8 , 9 , 10 ], but, as per our exhaustive search, these methodologies are not yet exploited to study the dynamics of PDI. Thus, it seems promising to exploit the well-established strength of machine learning and artificial intelligence techniques to study the dynamics of PDI.…”

“…Reference dataset generation: First, the reference dataset for the INSNs is generated through determining the numerical results of the PDI models presented in (7) to (9). The state of the art Adams procedure is used to determine the numerical results of the PDI models of ( 7) to ( 9) through the 'NDSolve' routine of Mathematica software for finding the solution of the systems represented by the differential equations for t ∈ [0, 5], with a step size 0.2, i.e., total 251 input (time instances), and, accordingly, a 753 output (number of measurements) with 251 discrete instances for each y 1 , y 2 , y 3 .…”

“…Developing neuro-supervised networks: The INSNs are constructed through a neural networks structure with logistic activation function to solve the PDI models of ( 7) to (9). For backpropagation, two different optimization algorithms are used, i.e., Levenberg-Marquardt (LM) and Bayesian regularization (BR).…”

Smart Nutrient Deficiency Prediction System for Groundnut Leaf

“…Asmita Mahajan et al, developed an ensemble model for the prediction of infectious diseases. Mahajan et al, stated that amalgamation of multiple models provides better performance than using a single model [17].…”