Fuzzy Regression-Based Statistical Modelling of Cardiovascular Risk Factors Using Indian Healthcare Data

Document Type : Research article

Authors

1 Department of Mathematics, Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu, India

2 Department of Mathematics Education, Farhangian University, P.O. Box 14665-889, Tehran, Iran

3 Payame Noor University (PNU), Tehran, Iran

4 Department of Physics, Sar.C., Islamic Azad University, Sari, Iran

Abstract

Cardiac disease is a major public health concern in India, and accurate statistical techniques are required for early risk assessment. Traditional regression analysis generally depends on fixed clinical thresholds; however, cardiovascular risk factors such as blood pressure, cholesterol, diabetes, obesity, tobacco use, and physical activity involve uncertainty because they gradually move from the absence of risk to the presence of risk. This study applies fuzzy regression to develop a statistical modelling framework for analysing cardiovascular disease risk factors using the Heart Attack Risk and Prediction dataset from Kaggle. The proposed modelling process includes data preprocessing, descriptive analysis, fuzzy membership construction, fuzzy regression modelling, and comparison with conventional regression models. The selected clinical and lifestyle variables are transformed into linguistic risk levels such as low, moderate, and high risk, thereby allowing the uncertainty associated with individual risk profiles to be represented more meaningfully. The performance of the models is evaluated using statistical measures such as mean absolute error, root mean square error, accuracy, sensitivity, specificity, and receiver operating characteristic area under the curve. The fuzzy statistical modelling approach improves interpretability and provides a flexible risk assessment structure compared with conventional modelling methods. Overall, this study presents a fuzzy regression-based statistical framework to support data-driven cardiovascular risk assessment in the Indian healthcare context.

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References

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Analytical and Numerical Solutions for Nonlinear Equations
Volume 11, Issue 2
September 2026
Pages 289-303

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History

  • Receive Date: 22 May 2026
  • Revise Date: 18 June 2026
  • Accept Date: 18 June 2026
  • Publish Date: 04 September 2026

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