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This study looks at statistics on heart disease mortality from the Institute for Health Metrics and Evaluation (IHME) from 1990 to 2021, divided into five age groups: 0\u20135, 6\u201315, 16\u201349, 50\u201369, and 70\u2009+\u2009. We used both classic ARIMA and hybrid models that combined ARIMA with machine learning techniques such as Random Forest, Support Vector Machine (SVM), XGBoost, and GARCH to anticipate mortality trends. Model performance was assessed using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Across several age groups, the ARIMA\u2009+\u2009SVM model outperformed standalone ARIMA in terms of accuracy, with RMSE improvements of up to 15.6%. The 70\u2009+\u2009population has the greatest mortality rates, highlighting the urgent need for focused healthcare treatments. These hybrid models are valuable tools for healthcare legislators in developing preventative programs, allocating resources effectively, and prioritizing treatment for high-risk age groups, especially the elderly, since they improve forecasting accuracy and offer interpretive insights. Given India's growing cardiovascular disease load, our results highlight how predictive analytics may support data-driven public health planning.<\/jats:p>","DOI":"10.1186\/s12889-025-23318-7","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T02:30:21Z","timestamp":1749522621000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis"],"prefix":"10.1186","volume":"25","author":[{"given":"M Darshan","family":"Teja","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G Mokesh","family":"Rayalu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"23318_CR1","unstructured":"Institute for Health Metrics and Evaluation (IHME). 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Therefore, ethical approval and informed consent were not required.\u00a0This manuscript does not contain any reproduced or adapted images. All figures and visual elements are original and created by the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Content for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"2150"}}