| Summary: | 碩士 === 國立中正大學 === 醫療資訊管理研究所 === 101 === Since 1995, suicide has been listed as the ten leading cause of death for 11 years, and suicide prevention is also further listed as the annual objective of medical quality and patient safety in 2012 and 2013. Health institutions hope to establish a more comprehensive assessment and care mechanism, but there is no significant effect. We can not grasp the cases who have intentions of committing suicide, so we can not also effectively prevent suicide behaviors. Past studies found that the suicide cases are mostly associated with their diseases, especially mental diseases. There are also studies suggest that the mental patients who have suicide records have higher probabilities of committing suicide again. Therefore, for the units of suicide prevention, how to predict the re-suicide of mental patients is a very important issue of public health.
Past relevant literature mostly take the suicide cases of a single hospital as the samples, and use versatile suicide assessment scales to predict whether a mental patient belongs to the group of having high risk of repeatedly committing suicide. The result has some errors due to we may not be able to exactly grasp the history of diseases of mental patients. Besides, the post-records of suicide reports can not achieve the purpose of early preventing the suicide behaviors of mental patients. For the above reason, this study takes LHID2010 of million people who have ever been diagnosed as mental patients as the basis, and selects the Eigen values of re-suicide patients which fit in with the research purpose, and use the decision tree of data mining technique and neural network technique to establish the repeated suicidal predictive model, and finally use Adaboost to build multiple classifier to enhance the predictive performance.
In terms of average accuracy rate of single classifier, we get the best effectiveness (92.54%) while adopting the original dataset and neural network to predict re-suicide. After adding Adaboost, the average accuracy rate of prediction reaches the highest standard (96.55%) while only retaining the decision tree classification model of dataset of significant variables. Therefore, the experimental result of this study proves that to adopt the decision tree and Adaboost to establish the prediction model of the re-suicide behaviors of mental patients can accurately predict the re-suicide behaviors of mental patients according to their medical behaviors. This study aims at assisting clinical personnel to early reach the purpose of preventing suicide behaviors.
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