A Study of Applying Artificial Intelligence for the Assessment of Maternal Depression

• Main Authors: Kuan-Chih Wang, 王冠智
• Other Authors: 張俊郎
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/r684n3

碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 98 === Suicide has been one of the ten major leading causes of death in Taiwan recently. According to the statistics, 87% of the cases suffered from depressions prior to their death, while 15% of the patients with depressions died from committing suicides. Depression is a very common emotional problem at the time being. Since one cannot determine on his/her own and professional physicians’ diagnosis and assessment is crucial in determining whether one has depression or not, how to effectively construct a set of comprehensive auxiliary diagnosis system to help doctors in their clinical assessment is becoming more and more important. Although women receive more cares during their pregnancy in general, due to the physical and psychological changes, they are more prone to suffer emotional swifts leading to prenatal depressions. When left alone without early treatment and intervention, it is very likely for these women to have postpartum depressions where in the worst case, they harm themselves and the infants. In this study, we aimed to use the decision tree and artificial neural network of the artificial intelligence to construct a diagnosis system in screening prenatal depressions. The objective is to find out the risk factors and symptoms that affect prenatal depressions for physicians to use as a reference, hoping to effectively reduce human judgmental errors in future medical assessment. The results in this study indicate that when using BPN to predict, accuracy of the risk factors for prenatal depressions is 83.33%, and the ROC area of the health care evaluation index is 0.819. Among all factors, the most significant ones include employment status, education background, birth place (city), pregnancy weeks, other diseases, and hours of sleep; these factors have great impact on prenatal depressions. Furthermore, with the use of decision trees, two rules of depression symptoms are generated at 86.67% in the accuracy and 0.861 for the ROC area. The results also reflect that depression patients are more likely to feel like crying and harming themselves when they are unhappy. The predictive system established in this study can provide a reference for physicians to diagnose prenatal depression, help identify the group at high risk so preventative measurements and treatments can be provided and thus practically beneficial in enhancing the clinical diagnosis accuracy.