Assessment of Risk Factors of Cardiovascular Diseases Using Artificial Neural Network (ANN): A Pilot Study

PGN Swamy; Kanika Rai

doi:10.55489/njcm.161120255963

Authors

PGN Swamy Chitkara School of Health Sciences, Chitkara University, Rajpura, Punjab, India; Adichunchanagiri Nursing College, Adichunchanagiri University, BG Nagara, Karnataka, India
Kanika Rai Chitkara School of Health Sciences; Centre for Evidence Based Practice in Health Care, Chitkara University, Punjab, India

DOI:

https://doi.org/10.55489/njcm.161120255963

Keywords:

Mixed method, Artificial neural network, Cardiovascular disease, Urban India, AI in Public health, CVD epidemiology

Abstract

Background: Cardiovascular diseases (CVDs) are the leading cause of death globally, particularly in urban populations. In India, an estimated 62.5 million lives are lost prematurely due to CVD. Traditional risk assessment tools like the Framingham Risk Score, Systematic Coronary Risk Evaluation, and Reynolds Risk Score are widely used. However, Artificial Neural Networks (ANNs) may provide improved prediction of cardiovascular risks. This study aims to assess cardiovascular risk factors among urban populations using ANN models and compare their effectiveness with traditional methods.

Methodology: A sequential exploratory mixed-method approach was used. The qualitative phase included focus group discussions with 35 healthcare professionals to identify perceived cardiovascular risk factors. Analysis via QDA Miner Lite highlighted health education, screening, and training as crucial roles of health workers, especially concerning diabetes, diet, and hypertension. The quantitative phase used a multi-layer perceptron model to analyze data from 60 participants (pilot phase), divided into 77% training and 23% testing datasets.

Results and Conclusion: The ANN model achieved 100% training accuracy and 85.7% testing accuracy, with an AUC of 0.969, showing strong predictive performance. The model effectively identified moderate-risk individuals, suggesting that ANNs outperform traditional methods in cardiovascular risk prediction.

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