An integrated decision tree-artificial neural network hybrid to estimate clinical outcomes : ICU mortality and pre-term birth

Yu, Na

doi:10.22215/etd/2010-09185

Cited by 3 publications

(52 citation statements)

References 85 publications

(216 reference statements)

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“…The third objective was to evaluate the above hypothesis; by comparing the sensitivity metrics obtained in this work with those obtained from past research (Ong [24], Yu [27] and Catley [11]). The same machine learning tools were used (DT and ANN) in past work performed [24] and the results obtained were compared to the current prediction performance, when applying new data preparation methods.…”

Section: Thesis Objectivesmentioning

confidence: 99%

“…The model was validated using the PRAMS database and this integrated classifier could predict mortality rates with a sensitivity of 65% and a specificity of 84%. The data preparation methods used in this thesis work were similar to Catley's: deletion of features with greater than 50% missing values, and the use of the k-NN CBR for imputation of missing values [27].…”

Section: Yumentioning

confidence: 99%

“…In previous work done by Catley [39] and Yu [27] the ANN was created using MATLAB software; however, MATLAB's Neural Network Toolbox is not open source software and there was difficulty in integrating it manually into the real time PPADS system. Previous work [85] focused on implementing a multilayer feed-forward-backpropagation ANN.…”

Section: Fast Artificial Neural Network Librarymentioning

confidence: 99%

“…All features which contained greater than 50% of missing values were removed. This followed steps taken in the theses of Ong [24] and Yu [27]. Applying…”

Section: Step 2: Eliminating Cases and Featuresmentioning

confidence: 99%

“…Conversely, due to the high number of term cases, there are few preterm cases which could be classified as term, the focus of this research is to limit this occurrence and contributes to the high negative predictive value (2) reported in the below tables. The ROC Curves for each dataset (Figure 4 [24], [27], [11], and resulted in faster computational time building the ANN models and improved accuracy of the classifier during training and testing. In addition, the second step of filling in missing values using a package in R (missForest [48]), proved faster than a case-based reasoning approach to fill in missing values.…”

Section: Step 8: Execution Of the Ann Buildermentioning

confidence: 99%

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Applying Data Preparation Methods to Optimize Preterm Birth Prediction

Esty¹

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The purpose of this work was to develop an accurate prediction model which can process information contained in antenatal databases to determine whether a baby will be born prematurely. The focus was on improved data preprocessing to add to methods developed by previous students in the Carleton MIRG (Medical Information technology Research Group) lab.The machine learning classifiers used included Decision Tree (DT) classifiers (for feature reduction) and the Artificial Neural Network (ANN) classifier (for model evaluation).Missing values and class imbalance was dealt with by applying software packages in the R statistical programming language.This research has shown a marked improvement in the accuracy of predicting preterm births.The final sensitivity and specificity results for the BORN (Better Outcomes Registry and Network) database were: Parous 89.2%, and 67.8%, Nulliparous 89.0% and 71.5%, and for PRAMS (Pregnancy Risk Assessment Monitoring System) database: Parous 84.1% and 71.4%, Nulliparous 83.8% and 76.0%. These improved results are promising. An accurate predictive tool will allow caregivers to implement preventative treatment strategies or to ensure delivery occurs in a tertiary health care Centre.iii

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Section: Thesis Objectivesmentioning

confidence: 99%

Section: Yumentioning

confidence: 99%

Section: Fast Artificial Neural Network Librarymentioning

confidence: 99%

“…All features which contained greater than 50% of missing values were removed. This followed steps taken in the theses of Ong [24] and Yu [27]. Applying…”

Section: Step 2: Eliminating Cases and Featuresmentioning

confidence: 99%

Section: Step 8: Execution Of the Ann Buildermentioning

confidence: 99%

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Applying Data Preparation Methods to Optimize Preterm Birth Prediction

Esty¹

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A web-based perinatal decision support system framework using a knowledge-based-approach to estimate clinical outcomes : neonatal mortality and preterm birth in twins pregnancies

Gunaratnam¹

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The author has granted a non exclusive license allowing Library and Archives Canada to reproduce, publish, archive, preserve, conserve, communicate to the public by telecommunication or on the Internet, loan, distrbute and sell theses worldwide, for commercial or non commercial purposes, in microform, paper, electronic and/or any other formats. AVIS: L'auteur a accorde une licence non exclusive permettant a la Bibliotheque et Archives Canada de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par telecommunication ou par I'lnternet, preter, distribuer et vendre des theses partout dans le monde, a des fins commerciales ou autres, sur support microforme, papier, electronique et/ou autres formats. Bien que ces formulaires aient inclus dans la pagination, il n'y aura aucun contenu manquant.

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An Integrated Machine Learning Approach to Optimize the Estimation of Preterm Birth

Ong¹

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This thesis describes a new methodology used in conjunction with artificial intelligence tools to create multiple models for prediction of preterm birth in obstetrical environments. The data mining approach integrates: Decision Trees (DTs), Artificial Neural Networks (ANNs) -specifically a Feed Forward Back Propagation ANN, and Case Based Reasoning System (CBRS).This work also introduces a 5by2 cross validation method, assesses two methods of attribute selection, and considers data prevalence (15% and 8.1%) in training and testing networks. Two databases were assessed from two countries: BORN (Canada) and PRAMS (USA). Best BORN results used selection method 2, had sensitivities of 50.53%, 53.96%, specificities of 91.61%, 95.40%, and area under curves (AUC) of 0.7721, 0.7970 for Parous and Nulliparous cases respectively. Best PRAMS results used selection method 1, had sensitivities of 68.15%, 40.35%, specificities of 64.71%, 94.57%, and area under curves (AUC) of 0.8452, 0.7064 for Parous and Nulliparous cases respectively. iii Above all, challenge yourself. You may well surprise yourself at what strengths you have, what you can accomplish. -Cecile Springer iv AcknowledgementsFirst and foremost, I would like to take this opportunity to thank my thesis supervisor, Dr. Monique Frize, for her support, encouragement and guidance. I am very grateful to have had the opportunity to work with you.

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An integrated decision tree-artificial neural network hybrid to estimate clinical outcomes : ICU mortality and pre-term birth

Cited by 3 publications

References 85 publications

Applying Data Preparation Methods to Optimize Preterm Birth Prediction

Applying Data Preparation Methods to Optimize Preterm Birth Prediction

A web-based perinatal decision support system framework using a knowledge-based-approach to estimate clinical outcomes : neonatal mortality and preterm birth in twins pregnancies

An Integrated Machine Learning Approach to Optimize the Estimation of Preterm Birth

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