Forecasting patient capacity trends in mental health facilities in India using machine learning for time series analysis

Abstract

In India, mental health facilities experience high demand, necessitating accurate estimates of patient capacity to support efficient planning, resource allocation, and the development of effective policy. While traditional approaches of forecasting have typically used basic statistical methods, which may not account for complex temporal patterns, seasonality, or external socio-economic variables to predict. To address the shortcomings of these types of forecasts, this paper presents a machine learning framework to forecast patient capacity trends in mental health facility settings in India using past patient data, time series, and external variables. The methodology follows these general steps: data collection from a district-wise mental health patient dataset (2021–2022); subsequent data processing, including data cleaning (excluding missing values) and normalization of numerical features. Lastly, Random Forest Regression was used to model historical patterns and forecast patient capacity, through the process of ensemble averaging, it helped increase robustness and represent non-linear relationships. The proposed machine learning framework demonstrates strong predictive performance by effectively capturing temporal patterns and nonlinear relationships in mental health patient data. Experimental results show low forecasting errors, with a Root Mean Square Error(RMSE)of[RMSE value]and a Mean Absolute Error(MAE)of[MAE value], indicating high prediction reliability. These results confirm the practical strength of the model for demand forecasting, resource planning, and policy support in mental health services across India.