Key result
An adaptive neural network beats conventional approaches with ~95% accuracy for early CVD detection.
Why the study?
Cardiovascular diseases are the foremost cause of mortality globally, motivating precise and prompt prediction to enable proactive measures.
Does an adaptive neural network integrating a fuzzy inference system improve the accuracy of early cardiovascular disease detection compared to conventional approaches?
Does an adaptive neural network integrating a fuzzy inference system improve the accuracy of early cardiovascular disease detection compared to conventional approaches?
A novel hybrid framework combining fuzzy logic and artificial neural networks achieved high accuracy (95.2%) and sensitivity (98.9%) in predicting cardiovascular disease risk.
May support hybrid AI models for CVD screening research; leaves open prospective clinical validation before any practice change.
Cardiovascular diseases (CVD) stand as the foremost cause of mortality globally. This research focuses on identifying the likelihood of cardiovascular disease with precision and promptness, aiming to enable individuals to take proactive measures to mitigate its impact on mortality rates. This study presents a novel prediction model for early detection of cardiovascular disease, combining fuzzy techniques and artificial neural networks based on knowledge. Creating a fuzzy inference system (FIS) to predict CVD early involves establishing membership functions for risk factors that impact the likelihood of developing the condition. The neural fuzzy inference system (NFIS) utilizes training data structured around the n-dimensional function. The evaluation of the suggested adaptive neural network, integrating a fuzzy inference system, demonstrated superior performance compared to conventional approaches in accuracy (0.952), specificity (0.987), precision (0.978), and sensitivity (0.989). This study seeks to enhance the effectiveness of knowledge-based systems by utilising fuzzy logic and artificial neural networks in the early detection of cardiovascular disease.
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Padhy et al. (2025) studied Cardiovascular disease. Adaptive neural network integrating a fuzzy inference system vs. Conventional approaches was evaluated on Model performance (accuracy, specificity, precision, sensitivity). An adaptive neural network integrating a fuzzy inference system demonstrated superior performance for early detection of cardiovascular disease, achieving an accuracy of 0.952 and sensitivity of 0.989.
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