C2H-ADLTM: Adaptive Distributed Multi-Objective Deep Learning Model for Missing Data Imputation in Healthcare
TL;DR
A Crocuta Community Hunt Optimization-enabled adaptive distributed learning-based Bi-directional Long Short-Term Memory (C2H-ADLTM) model is initiated, which enables the C2H-ADLTM model to attain faster learning performance and handle long-term dependencies.
Abstract
Missing data refers to the absence of data points within the dataset, which can occur for several reasons and creates diverse impacts in healthcare applications. One of the frequently used methods for handling missing values is imputation techniques, which estimate and substitute missing values with probable ones based on known data points. However, these techniques may not incorporate complex medical data relationships, create training difficulties, and lack generalization ability. Consequently, to overcome these shortcomings, this research initiated a Crocuta Community Hunt Optimization-enabled adaptive distributed learning-based Bi-directional Long Short-Term Memory (C2H-ADLTM). The adaptive distributed learning concept enables the C2H-ADLTM model to attain faster learning performance and handle long-term dependencies. The designed C2H algorithm is deployed to fine-tune the hyperparameters of the proposed model for handling large and complex datasets with minimal training time. Moreover, the research utilized a multi-objective function, which emphasizes the robustness of the C2H algorithm for dealing with several types of missing data and ensures efficient as well as accurate imputation. When compared with the progressive methods, the experimental outcomes show that the proposed model attains superior imputation performance with a reduced MAE of 4.69, MSE of 4.83, Median absolute error of 2.76, [Formula: see text] score of 0.87, and RMSE of 4.34 for the Pima Indian diabetes dataset.