Key result
BA-ANFIS achieves ~98% accuracy in heart disease prediction, outperforming standard ANFIS and SVM.
Why the study?
Traditional diagnostic methodologies are often inadequate for the complexity of clinical datasets, and ANFIS requires proper parameter tuning to handle non-linear medical data.
Does the BA-ANFIS model improve heart disease prediction accuracy compared to standard machine learning models in the Cleveland Heart Disease dataset?
null (n=303)
Does the BA-ANFIS model improve heart disease prediction accuracy compared to standard machine learning models in the Cleveland Heart Disease dataset?
Absolute Event Rate: 98.07% vs 94.12%
The BA-ANFIS hybrid machine learning model demonstrates high accuracy (98.07%) in predicting heart disease, outperforming traditional algorithms by optimizing neuro-fuzzy parameters.
May support heart disease prediction accuracy; leaves open prospective validation before any clinical adoption.
One of the means to reliably foretell it is the timely receipt of the correct medical treatment in the initial phases of heart disease. One of the most prevalent causes of death in the world is still heart disease. Traditional diagnostic methodologies are often inadequate to accommodate the complexity and ambiguity baked into clinical datasets. This study employs the Adaptive Neuro Fuzzy Inference System (ANFIS) and the Bat Algorithm (BA) to efficiently and precisely identify cardiac issues. ANFIS is a fusion of fuzzy logic and artificial neural networks has difficulties with medical data due to its non-linear nature. But this requires proper tuning of its parameters to work its best. The specific idea is that the Bat Algorithm which imitates the echolocation behaviour of bats, optimizes these parameters to improve prediction accuracy of the ANFIS model. The global search features of BA provide an optimal solution for the ANFIS membership functions together with the ANFIS rule parameters bypassing the limitation of conventional optimization methods. We validate the proposed system with characteristics derived from a clinical dataset of heart disease, such as age, blood pressure, and cholesterol level. Experimental results show that BA-ANFIS achieves an Accuracy of 98.07%, Sensitivity of 97.67%, and Specificity of 98.23%, outperforming baseline models including SVM and standard ANFIS by 86.28% and 94.12%, respectively. This method shows high efficiency in predicting heart disease and can provide support for diagnosis for practitioners, which helps to improve the prognosis of patients and to decrease health care costs due to BA global optimization and ANFIS adaptive reasoning capabilities.
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S et al. (2026) conducted a null in heart disease (n=303). BA-ANFIS vs. baseline models including SVM and standard ANFIS was evaluated on accuracy in predicting heart disease. BA-ANFIS achieved an accuracy of 98.07%, outperforming both SVM and standard ANFIS by 86.28% and 94.12% respectively in heart disease prediction.
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