Fingerprint is one type of physical evidence frequently encountered at a crime scene. It is useful in revealing identity of the culprit. However, poor quality latent fingerprint collected from a crime scene seldom makes an identification reliable. In practice, the identification is accomplished by matching a known print to an unknown print according to the types and locations of their minutiae features. When it is infeasible to conduct an identification, forensic scientist can attempt to predict the sex of donor of the latent fingerprint in order to narrow down the scope of searching of the suspect. In the context of forensic science, sexual dimorphism in ridge count has been studied for a few decades ago. Meanwhile, gender classification based on fingerprint images have been regularly reported in the field of computer science. Viewed from a practical perspective, image of a latent print collected from a real crime scene can be low in quality or even incomplete. Hence, this study has not considered image of fingerprint as input data but the number of diagonal ridge counted manually within a well-defined region, i.e. 25 centimeter squared. Firstly, the fingerprint data was explored using self-organizing maps method with respect to sexual dimorphism and ethnic difference. Next, Naïve Bayes (NB) and Classification and Regression Trees (CART) algorithms were, respectively, used to construct predictive model for discriminating gender based on the fingerprint data. A multitude of prediction models were constructed by considering ten-digit, five-digit and one-digit samples, respectively, to predict gender by three races, i.e. Chinese, Indians and Malays; and the combined sub-population. Each of the models was validated using bootstrapping without replacement approach. Results showed that the single-digit samples produced accuracy rate slightly lower than that obtained using five- or ten-digit samples. Comparing to the global predictive model, ethnicity-specific models of Indian and Malay subjects showed slight improvement in external accuracy rate. Moreover, by considering all five digits of a particular hand as input data, NB tends to outperform CART. However, both NB and CART are comparable to each other when one-digit sample was considered as input data. In conclusion, SOM is useful for exploring sexual dimorphism of fingerprint data and NB outperforms CART in modelling of the fingerprint data.