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Preliminary Modeling of Gender Identification in the Social Community

Breast cancer is one of the most common cancers in women around the world. In Taiwan, the number of women suffering from breast cancer is increasing year by year. Furthermore, the incidence rate and mortality rate of breast cancer in women were ranked first and fourth, respectively, but its free screening rate had the lowest proportion of mammography examination usage. That statistic is due to the breasts being a very private part of the body and women being unlikely to get the screening done due to social perceptions and poor awareness of the disease. Therefore, the ideal timing for getting early treatment is often missed. This study was initiated to collect the perceptions of the general public about breast cancer screening by way of the comments and replies to any query or experience sharing left on different social media websites (PTT, Mobile01, and Facebook). Using the text mining technique to catch important features and connect them to different personalities of people, the identification model can be established by CART (Classification and Regression Tree) machine learning. The model will then be applied to explore the lifestyles and personalities of those people concerned about breast cancer screening so that different prevention and management policies can be proposed to meet their different needs. The study experiment example was based on gender identification to proceed with the model simulation and forecast accuracy.