Using Data Mining Techniques to Establish Prediction Model of Bipolar Disorder for Unipolar Depression Patients

• Main Authors: Chun Yi Wu, 吳俊儀
• Other Authors: Ya-Han Hu
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/hnbrnd

碩士 === 國立中正大學 === 資訊管理學系暨研究所 === 102 === Over the last few decades, unipolar depression has been the second leading cause of disability. Clinical features of unipolar depression and bipolar disorder, do not readily differentiate the two illness trajectories in the early course of illness. Although a lot of research work has been done in this filed to seek for reliable measurement, there is no clear direction to distinguish bipolar disorder from unipolar depression. Antidepressants are drugs used for the treatment of unipolar depression. Because the mood swings are less obvious from depression to manic episodes, many bipolar disorder patients are often wrongly treated with antidepressants alone. Treating these patients with antidepressants alone can actually increase the manic episodes and worsen the disorder. Our purpose of this study is to explore the comorbidity symptoms of unipolar depression (UD) patients who are developing into bipolar disorder. The method to carry out this study is using data mining with WEKA decision trees、artificial neural network and logistic regression. The data consisted of 5830 patients with a history of depression from the National Health Insurance Research Database in Taiwan during 2003 to 2010. The results show that 73 of 5830 patients who are diagnosed with depression actually suffering from bipolar disorder (BD). We extract 30 random sample sets from 5757 UD patients. Each set includes 4 percent of 5757 UD patients and is merged with 73 BD patients. We use 30 sets to run the WEKA classifier and the results show that decision tree is significantly superior to artificial neural network and logistic regression. We get the average accuracy rate of decision tree is 73.4%. We can accurately predict patients who have comorbidity symptoms, such as personality disorders, drug addiction, adjustment disorder, alcohol dependence syndrome, anxiety disorder and neurotic disorders, could have a greater chance of developing into bipolar disorder. Therefore, the experimental result of this study proves that the comorbidity symptoms described above were beneficial to explore the potential patients who suffering from bipolar disorder. This study also demonstrated that unhealthy patient behaviors were also increased the risk of developing bipolar disorder.