Regression Modelling and Forecasting for Indian Suicide Cases
Abstract
Objective: The issue of suicide has become a topic of public health concern in India. Mitigating the repercussions associated with suicide has become an imperative focus of public health initiatives. Proactive
identification of suicide risks, informed by surveillance data, is crucial for managing the incidence of suicide fatalities in India. Our research endeavors were aimed at exploring the feasibility of employing a Quadratic model to forecast the incidence of suicide deaths in India.
Method: The dataset comprising annual suicide death cases in India from 1990 to 2016 was utilized to calibrate the Quadratic model. The discrepancy between the observed and predicted values was employed
to assess the forecasting accuracy of the developed replicas.
Results: Mean suicide rate remained determined to be 20.11789, with a standard deviation of 1.194, a minimum of 17.8963, and a maximum of 21.4266 individuals. The Quadratic model demonstrated statistical
significance (p < 0.001) and passed the correlation residual test (p >0.05), exhibiting a high Adjusted R² and R² alongside low values of Mean Squared Error, Mean Absolute Error and Root Mean Squared Error and Average Forecast Error values at 0.961, 0.975, 0.004624, 0.322470, 0.39681, and 0.015475, respectively.
Conclusions: The Quadratic model emerged as the most suitable model for predicting suicide death cases in India.
Keywords: Suicide, Epidemiology, Regression, Quadratic, Curve,Prevention
How to cite this article:
Thangarasu D, Murugesan T, Radhakrishnan A, Rajasekar V D. Regression Modelling and Forecasting for Indian Suicide Cases. Chettinad Health City Med J. 2026;15(2):4-9.
DOI: https://doi.org/10.24321/2278.2044.202609
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