title: Prediction of periventricular leukomalacia. Part I: Selection of hemodynamic features using logistic regression and decision tree algorithms.
abstract: OBJECTIVE
  Periventricular leukomalacia (PVL) is part of a spectrum of cerebral white matter injury which is associated with adverse neurodevelopmental outcome in preterm infants. While PVL is common in neonates with cardiac disease, both before and after surgery, it is less common in older infants with cardiac disease. Pre-, intra-, and postoperative risk factors for the occurrence of PVL are poorly understood. The main objective of the present work is to identify potential hemodynamic risk factors for PVL occurrence in neonates with complex heart disease using logistic regression analysis and decision tree algorithms.
  
  
  METHODS
  The postoperative hemodynamic and arterial blood gas data (monitoring variables) collected in the cardiac intensive care unit of Children's Hospital of Philadelphia were used for predicting the occurrence of PVL. Three categories of datasets for 103 infants and neonates were used-(1) original data without any preprocessing, (2) partial data keeping the admission, the maximum and the minimum values of the monitoring variables, and (3) extracted dataset of statistical features. The datasets were used as inputs for forward stepwise logistic regression to select the most significant variables as predictors. The selected features were then used as inputs to the decision tree induction algorithm for generating easily interpretable rules for prediction of PVL.
  
  
  RESULTS
  Three sets of data were analyzed in SPSS for identifying statistically significant predictors (p<0.05) of PVL through stepwise logistic regression and their correlations. The classification success of the Case 3 dataset of extracted statistical features was best with sensitivity (SN), specificity (SP) and accuracy (AC) of 87, 88 and 87%, respectively. The identified features, when used with decision tree algorithms, gave SN, SP and AC of 90, 97 and 94% in training and 73, 58 and 65% in test. The identified variables in Case 3 dataset mainly included blood pressure, both systolic and diastolic, partial pressures pO(2) and pCO(2), and their statistical features like average, variance, skewness (a measure of asymmetry) and kurtosis (a measure of abrupt changes). Rules for prediction of PVL were generated automatically through the decision tree algorithms.
  
  
  CONCLUSIONS
  The proposed approach combines the advantages of statistical approach (regression analysis) and data mining techniques (decision tree) for generation of easily interpretable rules for PVL prediction. The present work extends an earlier research [Galli KK, Zimmerman RA, Jarvik GP, Wernovsky G, Kuijpers M, Clancy RR, et al. Periventricular leukomalacia is common after cardiac surgery. J Thorac Cardiovasc Surg 2004;127:692-704] in the form of expanding the feature set, identifying additional prognostic factors (namely pCO(2)) emphasizing the temporal variations in addition to upper or lower values, and generating decision rules. The Case 3 dataset was further investigated in Part II for feature selection through computational intelligence.
authors.affiliation: Department of Mechanical Engineering, Villanova University, PA 19085, USA. biswanath.samanta@villanova.edu
authors.forename: Biswanath
authors.initials: B
authors.lastname: Samanta
authors.forename: Geoffrey L
authors.initials: GL
authors.lastname: Bird
authors.forename: Marijn
authors.initials: M
authors.lastname: Kuijpers
authors.forename: Robert A
authors.initials: RA
authors.lastname: Zimmerman
authors.forename: Gail P
authors.initials: GP
authors.lastname: Jarvik
authors.forename: Gil
authors.initials: G
authors.lastname: Wernovsky
authors.forename: Robert R
authors.initials: RR
authors.lastname: Clancy
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Licht
authors.forename: J William
authors.initials: JW
authors.lastname: Gaynor
authors.forename: Chandrasekhar
authors.initials: C
authors.lastname: Nataraj
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002245:Carbon Dioxide / Q000032:analysis; D003663:Decision Trees; D006330:Heart Defects, Congenital / Q000150:complications* / Q000503:physiopathology / Q000601:surgery; D006439:Hemodynamics; D006801:Humans; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal; D007969:Leukomalacia, Periventricular / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology; D016015:Logistic Models; D011184:Postoperative Period; D012372:ROC Curve; D012307:Risk Factors
chemical_list: D002245:Carbon Dioxide