@inproceedings{scholars12632,
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {2020 International Conference on Computational Intelligence, ICCI 2020},
           title = {Comparison between Conventional and Fuzzy Hypotheses Test Results for Parameter Treatment Effect for Heart Patients},
           pages = {217--222},
            note = {cited By 0; Conference of 2020 International Conference on Computational Intelligence, ICCI 2020 ; Conference Date: 8 October 2020 Through 9 October 2020; Conference Code:164916},
            year = {2020},
             doi = {10.1109/ICCI51257.2020.9247733},
        keywords = {Acceptance tests; Intelligent computing; Population statistics, Fuzzy decision; Fuzzy hypothesis; Heart patients; Hypothesis tests; Null hypothesis; Parameter effects; Standard deviation; Treatment effects, Patient treatment},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097546130&doi=10.1109\%2fICCI51257.2020.9247733&partnerID=40&md5=354d3023e1c1df9e062d2524f98abc9b},
        abstract = {In the traditional hypotheses test, hypotheses are crisp. In this paper, we consider the hypotheses test for unknown mean in normal populations with fuzzy data when the standard deviation of the population is known. This paper aims to distinguish various parameter effects on clinical Heart Patients with Two-way Anova, and in this fuzzy test, we will make a fuzzy decision for rejection or acceptance null hypothesis on various parameters of clinical data of Heart Patients with Fuzzy p-value and compared the results with the conventional hypothesis test results. These results will be a benchmark for new patients (same characteristics as the old patients) to treat them in a better way. {\^A}{\copyright} 2020 IEEE.},
          author = {Gandikota, N. S. K. and Hasan, M. H. and Jaafar, J.},
            isbn = {9781728154473}
}