Recommendation of Primary Healthcare Centers through estimating Quality of Services using Momentum Gradient Descent based Multilayer Perceptron

Abstract

The prediction of the quality services tends to both service improvement as well as enhanced motivation to visit Primary Healthcare Centres (PHC) for health treatment. There are various estimation tools have been designed to evaluate the quality of the PHC, however, no particular tool is identified in this area. Hence, this research performed to develop as well as estimate the quality assessment tool of PHC. This research proposes the Momentum Gradient Descent based Multilayer Perceptron (MGD-MLP) for the recommendation of PHC and estimating the Quality of Services (QoS) by using SERVQUAL tool. Initially, the data is collected from the medical college hospital, 14 Government taluk hospitals, 75 PHCs, and 252 health sub-centres in Pudukkottai district in the year of 2019 to 2020. The Cronbach alpha technique is utilized for estimating an internal consistency of every model factor. The MGD accelerates the convergence of the optimization process, minimizing the training time to train the model, so it enhances the model performance. The experimental results show that the proposed MGD-MLP approach attains the MSE of 0.119, MAE of 0.205, RMSE of 1.198, and R2 of 3.893 respectively.