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@article{198305,
        author = {Kunal Bisane and Mantasha Sayyed and Ayesha Sayyed and Nikhil Bhujade and Prof. Rakesh Jambhulkar},
        title = {HEART DISEASE DETECTION USING NEUROFUZZY APPROACH},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {8360-8363},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198305},
        abstract = {heart disease remains one of the leading causes of mortality worldwide. Early and accurate detection is crucial for effective treatment and prevention. Traditional diagnostic methods often rely on clinical expertise and are prone to subjectivity. This paper presents aNeurofuzzy-based approach for heart disease detection that combines the learning capabilities of neural networks with the reasoning ability of fuzzy logic. The proposed system takes patient clinical parameters as input and outputs a risk percentage along with a binary classification (High Risk / Low Risk). A web-based application has been developed that stores user information and prediction history in a MySQL database, and generates downloadable PDF reports. The system achieves high accuracy and interpretability, making it suitable for real-world clinical decision support.},
        keywords = {Neurofuzzy, Heart Disease Detection, Fuzzy Logic, Neural Networks, Risk Prediction, Healthcare Informatics.},
        month = {April},
        }