Time-series Forecasting of Hypertension Prevalence using Arima, Lstm, and Hybrid Arima–lstm Models

D P Singh

doi:10.36948/ijfmr.2026.v08i04.85559

Time-series Forecasting of Hypertension Prevalence using Arima, Lstm, and Hybrid Arima–lstm Models

Author(s)	Dr. D P Singh
Country	India
Abstract	Hypertension is a major global risk factor for cardiovascular disease and premature mortality, with increasing public health implications in India. This study compares ARIMA, LSTM, and Hybrid ARIMA–LSTM models for forecasting India’s hypertension burden using annual data from 1990–2025 across six epidemiological indicators: prevalence, prevalence cases, ASPR, incidence, deaths, and DALYs. After data pre-processing and stationarity testing, ARIMA captured linear trends, LSTM modeled nonlinear patterns, and the hybrid model integrated both approaches. Model performance was evaluated using MAE, RMSE, MAPE, R², and residual diagnostics. The Hybrid ARIMA–LSTM model consistently achieved superior forecasting accuracy, with lower errors and better goodness-of-fit than the standalone models. Forecasts for 2026–2045 suggest that hypertension will remain a significant health challenge, with prevalence reaching 30.87% and affecting approximately 350.11 million people by 2045, along with 13.83 million incident cases, 217.77 thousand deaths, and 5.54 million DALYs. The findings highlight the need for strengthened prevention, early detection, disease management, and evidence-based health policies, while supporting the Hybrid ARIMA–LSTM framework as a robust tool for long-term epidemiological forecasting.
Keywords	Hypertension; Time-Series Forecasting; ARIMA; LSTM; Hybrid ARIMA–LSTM; Machine Learning; Deep Learning; Epidemiological Forecasting; Public Health; India; Disease Burden; Healthcare Planning.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-12
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.85559

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Time-series Forecasting of Hypertension Prevalence using Arima, Lstm, and Hybrid Arima–lstm Models

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