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New Technology of Library and Information Service  2016, Vol. 32 Issue (2): 83-89    DOI: 10.11925/infotech.1003-3513.2016.02.11
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Building Disease Prediction Model Using Support Vector Machine ——Case Study of Severe Acute Pancreatitis
Zhang Ye1,Zhang Han1,Yin Bincan1,Zhao Yuhong2()
1Department of Medical Informatics, China Medical University, Shenyang 110122, China
2Shengjing Hospital of China Medical University, Shenyang 110004, China
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[Objective] This study developed a disease prediction model based on the support vector machine, using electronic medical records of the severe acute pancreatitis patients. [Methods] We first adjusted the kernel type and parameter values of the support vector machine method to get an optimized prediction model. Then, we combined it with univariable and multivariable logistic regression analysis methods to select features’ variable. Finally, we proposed a simplified early warning model for the severe acute pancreatitis. [Results] The new model’s prediction accuracy rate is 70.37%. Variables used by this model include: white blood cell count, serum calcium, serum lipase, systolic blood pressure, diastolic blood pressure and pleural effusion. [Limitations] Because of the small sample size, we only used this support vector machine method to develop the new disease prediction model. In the future, we will try to establish a larger examination system for the clinical trial. [Conclusions] Support vector machine can help us develop an optimal disease prediction model. A new system based on this model could support our clinical decision makings.

Key wordsSupport Vector Machine      Severe acute pancreatitis      Early warning      Clinical decision     
Received: 21 September 2015      Published: 08 March 2016

Cite this article:

Zhang Ye,Zhang Han,Yin Bincan,Zhao Yuhong. Building Disease Prediction Model Using Support Vector Machine ——Case Study of Severe Acute Pancreatitis. New Technology of Library and Information Service, 2016, 32(2): 83-89.

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