Clinical Decision Support System for HCC using Classification Models

Objectives : The purpose of this paper is to construct and optimize performance of a classification model to aid management of Clinical Decision Support System and to evaluate performance implementation effectiveness and barriers to adoption Methods : We used 8 classification models including RF, SVM, k-nearest neighbor, linear discriminant analysis, logistic regression, boosting and ANN were trained and their performance were compared for predicting patient’s prognosis. Variable selection was performed and only clinical variables relevant to outcomes were utilized for predicting the outcome to avoid over fitting the model and to detect the surveillance clinical exam’s significance. After that we plan to compare the performance of RF, ANN and SVM to other classification results using ROC curves. Using several packages of Random forest(package for Random Forest), Support vector machine(package e1071) Shrunken centroid(package pamr) LDA9package(sml), KNN(package class), and logistic regression(package stats), Boosting(package boost), ANN(Neural networks package) we can get the results of the classification for clinical support system. Results : We find that several clinical variables and SNP data were selected as important factor for clinical decision of HCC patient prognosis and manage surveillance clinical exams using 8 classification models. For the comparison of models and methods all models were run evaluation step and validation step using 10 fold cross validation and the accuracy, sensitivity, specificity, positive predictive value and negative predictive value, ROC curves and area under ROC(AUC) with Mann –Whitney test. Conclusions : Random Forest seems to be overall the best performing model in terms of accuracy and balance of sensitivity and specificity. Using clinical decision support system Physicians can improve their diagnosis and decision making assisted and it could be efficient and cost effective management of medical resources and surveillance clinical exam.