Intelligent medical diagnosis via machine learning Chuan Lu Dept. of Electrical Engineering

1. Intelligentmedical diagnosis via machine learning Chuan Lu Dept. of Electrical Engineering Patient data Statistical analysis Variable selection Model building Model evaluation • Applications • Ovarian tumor classification • Ovarian cancer: difficult in early detection, the highest mortality rate in gynecologic cancers • Develop a reliable diagnostic tool for preoperative distinction between benign and malignant tumors • Assist clinician in choosing the appropriate treatments • Preoperative medical diagnostic methods: • serum tumor marker: CA125 blood test • ultrasonography • color doppler imaging • Data • Results: performance of the models given the selected 10 variables on the test set (160 newly collected data) • Brain tumor classification based MRS spectra data • 4 types of brain tumors, 205x138 magnitude value • performance increases from accuracy of 68.5% to 75.3% by using only 27 variables for the linear LS-SVM classifier • Cancer diagnosis based on microarray data • Classification of leukemia cancer and colon cancer • Zero LOO error was achieved by using only 4 or 5 genes among the available thousands of genes. • Introduction • Background • Medical decision support systems based on patient data and expert knowledge • A need to analyze the collected data in order to draw a correct medical decision • Intelligent machine learning methods such as artificial neural networks (ANNs) and kernel-based algorithms shown to be suitable approaches to such complex tasks. • Research topic • Statistical analysis of patient data • Development and clinical evaluation of predictive models which optimally extract information from data • Application toreal world clinical data, such as the ovarian tumor data set, as well as to other benchmark data sets in the biomedical field. • Methods • Explorative data analysis (EDA) • Variable (feature) selection • Important in medical diagnosis • economics of data acquisition • accuracy and complexity of the classifiers • gain insights into the underlying medical problem • Focus on evidence based method within the Bayesian framework • forward / stepwise selection • Bayesian LS-SVM • spares Bayesian learning • accounting for uncertainty in variable selection • Probabilistic modeling techniques • Dealing with the uncertainty and different mis-classification cost in medical decision support • Traditional linear discriminant analysis (LDA), logistic regression (LR) • Bayesian + multi-layer perceptrons (MLPs) • Bayesian + kernel based modeling: • Bayesian Least squares support vector machine (LS-SVM) classifiers (Suykens 1999,2001,2002) • Sparse Bayesian modeling and relevance vector machines (RVMs) (Tipping 2001, 2003) Biplot of Ovarian Tumor Data Collected in Unv. Hospitals Levuen (1994~1999), 425 records, 25 features, 32% malignant Visualizing the correlation between the variables and the relations between the variables and clusters ROC curves Performance of Bayesian RBF LS-SVM with rejection based on posterior probability • Conclusions • The intelligent machine learning methods, particularly Bayesian kernel based modeling and the related variable selection methods, are shown to have great potential value in medical diagnosis problems. Acknowledgements This research was funded by the projects of IUAP IV-02 and IUAP V-22, KUL GOA-MEFISTO-666, IDO/99/03, FWO G.0407.02 and G.0269.02, and a Research Council KUL doctoral fellowship. Further information Chuan Lu K.U.Leuven – Dept. ESAT Division of SCD-SISTA Kasteelpark Arenberg 10 3001 Leuven (Heverlee), Belgium chuan.lu@esat.kuleuven.ac.be Supervisors: Prof. Sabine Van Huffel Prof. Johan Suykens Tel.: +32 16 32 18 84 Fax: +32 16 32 19 70 www.esat.kuleuven.ac.be