Application of Multiple Linear Regression to Predicted the Duration of Outpatient Rehabilitation in Drugs Users (Case Study: MAB Rehabilitation Center)

Abstract

Narcotics abuse is a major issue that requires appropriate rehabilitation programs. The variation in outpatient rehabilitation duration among patients highlights the need for a prediction model to support rehabilitation planning. This study aims to apply the Multiple Linear Regression method to predict outpatient rehabilitation duration based on age, duration of narcotics use, frequency of use, and type of narcotic substance. The study used secondary data consisting of 800 outpatient records from the MAB Rehabilitation Center. Data processing was performed using Python in Google Colab with the Ordinary Least Squares (OLS) method through data preprocessing, dataset splitting into 80% training data and 20% testing data, and model evaluation using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), the coefficient of determination (R²), the t-test, and the F-test. The results show that the Multiple Linear Regression method can be applied to develop a prediction model for outpatient rehabilitation duration. However, the t-test and F-test indicate that all independent variables have no significant effect on rehabilitation duration. The R² value of 0.005 indicates that the model explains only 0.5% of the variation in rehabilitation duration.