Parameter estimation for logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets ML estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. This study extends logistic regression model to fuzzy logistic regression model by suggesting a new approach based on fuzzy concepts to estimate the model parameters. The proposed approach is expected to be an effective alternative to ML in case of small samples. A mathematical programming model which minimizes the total spread of the estimated probabilities of the logistic model is suggested and its performance is evaluated versus ML approach by a Monte Carlo simulation study. Results show that the new proposed model outperforms ML in case of small sample size.