Application of neaural network for prediction the cancer patients surviaval status from clinical records

作者：Wan Min^*; Fang Liying; Cheng Wenshuai; Wang Wanjing; Zhang Qixun

来源：Journal of Computational Information Systems, 2014, 10(17): 7425-7433.

DOI：10.12733/jcis11432

• Association analysis
• Clinical records
• Data mining algorithm
• K-nearest neighbors
• Multi-layer feed forward
• Non small cell lung cancer
• Pathological changes
• Traditional Chinese Medicine

In the study of rules in pathological changes, most of analysis view from static perspective, and regard cross-sectional data as input data set to analyze the effect of non-time-varying factors. However, according to the clinical experiences, the changes of physical state can partly reflect the disease progression and the death. Thus, based on the dynamic perspective, the changing patterns of symptom are considered as the model input, longitudinal data is utilized to further explore the rules in pathological changes. This study first proposed a data mining algorithm to find the correlation between patient's symptoms and death, which utilizes the Traditional Chinese Medicine (TCM) and the western medicine clinical records. Furthermore, the prediction of patient's cancer deterioration and death is achieved by using the multi-layer feed forward neural network (MFNN) model, where key symptoms are used as inputs and cancer deterioration and death conditions are used as outputs. The correctness and effectiveness of proposed k-NN and MFNN algorithms are verified by simulation results. The accuracy of data prediction using these data mining algorithms achieves about 94.66%. The experiment indicated that the proposed correlation association analysis and data prediction and pattern matching methods are feasible and effective. Meanwhile, the k-NN model is effective on predicting the next stage of key symptoms and finding out the most probability of patient's cancer deterioration and death for III stage non-small cell lung cancer patients, which is helpful to enhance the accuracy and efficiency of doctor's clinical diagnosis in practice.

• 出版日期2014
• 单位北京大学