Neutrosophic Structure of the Log-logistic Model with Applications to Medical Data

In practical scenarios, it is common to encounter fuzzy data that contains numerous imprecise observations. The uncertainty associated with this type of data often leads to the use of interval statistical measures and the proposal of neutrosophic versions of probability distributions to better handle such data. This study introduces a new generalized design of the log-logistic distribution within a neutrosophic framework, building upon encouraging applications of this distribution in fields such as economics, engineering, survival analysis, and lifetime modeling. The proposed neutrosophic loglogistic distribution (NLLD) is analyzed in terms of statistical properties, including moments, shape coefficients, and various survival characteristics. To evaluate the performance of the predicted neutrosophic parameters, an estimation procedure is conducted. Finally, the practical application of the proposed model is demonstrated using a sample dataset consisting of 128 bladder cancer patients.