Towards Prediction of Pancreatic Cancer Using SVM Study Model
Yushan Qiu, Hao Jiang, Kazuaki Shimada, Kensei Maeshiro, Wai‐Ki Ching, Kiyoko Flora Aoki-Kinoshita, Koh Furuta · 2014
Pancreatic cancer is known to be a difficult disease to diagnose early, and early research mainly focused on predicting the survival rate of pancreatic cancer patients. The correct prediction of the various disease states can greatly benefit the patient and also assist the design of effective and personalized therapeutics. The issue of how to integrate the available laboratory data with classification techniques is an important and challenging research issue. In this paper, we proposed a useful approach to construct a feature space which serves as a significant predictor for classification. Furthermore, we developed a novel method to identify the outliers which are important for improving the classification performance. Using our preoperative clinical laboratory data and histologically confirmed pancreatic cancer samples, computational experiments are performed with the use of Support Vector Machine (SVM) to predict the status of the patients. We further tested the method by employing the Multi-Layer Perceptron (MLP) kernel with a three-fold cross-validation to assess the predictive power of the selected features. Experimental results on the prediction of cancer state of patients indicate that our method performs very well on pancreatic samples obtained in the clinical environment.