Predictive model of life cycle medical care services in Peruvian health private entities - Mendoza-Montoya, Javier (2023)
A predictive model is a good technique for prognosticating the life cycle of medical care services (LCMCS) related to its growth, stability, and decline to support information processes and decision-making based on intelligent forecasts. Medical care services require organization, scheduling, and programming attention to prevent the capacity of medical personnel. Otherwise, it could be chaotic. This research aims to evaluate three predictive models to find the best one to fit information about the LC-MCS and understand medical care services' behavior in healthcare entities. The proposed model applied to Ricardo Palma Clinic (RPC) in Lima Perú analyzes the LC-MCS information based on 4950 clinical health records. Three predictive models were trained and compared to evaluate the accuracy of backpropagation neural networks, decision trees, and multiple linear regression models. After training, the coarse tree model gives a root mean squared error (RMSE) of 30.237 and an accuracy of 85%. The neural network model with ten hidden layers using the Sigmoid transfer function gives the best validation performance of 608083 at epoch 9; however, the Stepwise Linear regression model gives the best performance between the three trained models with a RMSE of 11.553 and 87.3% accuracy in predicts LC-MCS in Ricardo Palma Clinic. In conclusion, it is possible to predict the LC-MCS in Peruvian Healthcare entities and use tools such as stepwise linear regression to give real-time information about medical care services.