@inproceedings{2f12e06ffb124977b10a6afc820cb943,
title = "Prognosis of Cardio Risks Elements using Hybridized Neuro Fuzzy Perspective",
abstract = "The increasing amount of heart diseases occurring in today{\textquoteright}s generation has been enormous which has put up the need for the prediction of heart diseases that will improve patient outcomes, enhance healthcare efficiency and reduce the overall burden over the healthcare. In this study, a model is proposed using hybrid neurofuzzy technique including genetic algorithm in early prediction of heart diseases. The datasets are taken from the medical records of UIC. The study details all the input attributes and their membership functions as well comparing the proposed model with other models is shown through graphs. The outcome generated a training and testing accuracy of 0.92 and 0.87 respectively. The MSE error rate during training and testing phase was 0.117 and 0.16 respectively. Also, the f-score metric was found to be 0.91. Hence, the model can benefit the cardio risk patients in more reliable treatment.",
keywords = "Heart, Training, Accuracy, Uncertainty, Medical services, Predictive models, Prediction algorithms, Reliability, Prognostics and health management, Diseases, neurofuzzy, ANFIS, genetic algorithm, neural network, heart disease prediction",
author = "Ankita Samantaray and Aryabrat Mishra and Tiansheng Yang and Bharati Rathore and Danyu Mo and Lu Wang",
year = "2024",
month = oct,
day = "24",
doi = "10.1109/iacis61494.2024.10721959",
language = "English",
isbn = "979-8-3503-6067-7",
series = "2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS)",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS)",
note = "International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS ; Conference date: 23-08-2024 Through 24-08-2024",
}