本研究以民國84年間自臺灣北部某監獄出監的349位個案，追蹤其自離開監獄至92年12月31日止的妨害性自主罪再犯情形爲樣本。分析相關性較高的18個再犯因子後，利用類神經網路建構再犯預測模型，並與RRASOR、Static-99及MnSOST-R的receiver operating characteristic (ROC)曲線相比較以檢驗其預測能力。初步建構的類神經網路模型ROC曲線之AUC爲0.772（95%信賴區間：0.683~0.862，p<0.001），達統計上的顯著；與其他量表相較則有較佳的預測能力。類神經網路不失爲一個未來可再進一步運用的方法。

Participants of this study were 349 sexual offenders released from a prison in northern Taiwan in 1995, and we follow all cases from the time of release to December 31, 2003. 18 risk predictors with statistic significance are selected to construct a artificial neural network (ANN) model for the sexual offender recidivism prediction. Then we examined the predict ability of the ANN model by receiver operating characteristic (ROC) analysis, and compare with other common used screening tools for prediction of sex offender recidivism, i.e. RRASOR, static-99, and MnSOST-R. The area under the ROC curve for ANN model is 0.772 (95% CI: 0.683~0.862, p<0.001), which reach the statistic significance. Comparing with other screening tools, ANN model got better predict ability. It means that ANN is a considerable method for further research.