J Biomed Inform - Data mining methods for classification of Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) using non-derivatized tandem MS neonatal screening data.


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Newborn screening programs for severe metabolic disorders using tandem mass spectrometry are widely used. Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) is the most prevalent mitochondrial fatty acid oxidation defect (1:15,000 newborns) and it has been proven that early detection of this metabolic disease decreases mortality and improves the outcome. In previous studies, data mining methods on derivatized tandem MS datasets have shown high classification accuracies. However, no machine learning methods currently have been applied to datasets based on non-derivatized screening methods. A dataset with 44,159 blood samples was collected using a non-derivatized screening method as part of a systematic newborn screening by the PCMA screening center (Belgium). Twelve MCADD cases were present in this partially MCADD-enriched dataset. We extended three data mining methods, namely C4.5 decision trees, logistic regression and ridge logistic regression, with a parameter and threshold optimization method and evaluated their applicability as a diagnostic support tool. Within a stratified cross-validation setting, a grid search was performed for each model for a wide range of model parameters, included variables and classification thresholds. The best performing model used ridge logistic regression and achieved a sensitivity of 100%, a specificity of 99.987% and a positive predictive value of 32% (recalibrated for a real population), obtained in a stratified cross-validation setting. These results were further validated on an independent test set. Using a method that combines ridge logistic regression with variable selection and threshold optimization, a significantly improved performance was achieved compared to the current state-of-the-art for derivatized data, while retaining more interpretability and requiring less variables. The results indicate the potential value of data mining methods as a diagnostic support tool.

Resumo Limpo

newborn screen program sever metabol disord use tandem mass spectrometri wide use mediumchain acylcoa dehydrogenas defici mcadd preval mitochondri fatti acid oxid defect newborn proven earli detect metabol diseas decreas mortal improv outcom previous studi data mine method derivat tandem ms dataset shown high classif accuraci howev machin learn method current appli dataset base nonderivat screen method dataset blood sampl collect use nonderivat screen method part systemat newborn screen pcma screen center belgium twelv mcadd case present partial mcaddenrich dataset extend three data mine method name c decis tree logist regress ridg logist regress paramet threshold optim method evalu applic diagnost support tool within stratifi crossvalid set grid search perform model wide rang model paramet includ variabl classif threshold best perform model use ridg logist regress achiev sensit specif posit predict valu recalibr real popul obtain stratifi crossvalid set result valid independ test set use method combin ridg logist regress variabl select threshold optim signific improv perform achiev compar current stateoftheart derivat data retain interpret requir less variabl result indic potenti valu data mine method diagnost support tool

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