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

Abstract

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.

A review of approaches to identifying patient phenotype cohorts using electronic health records

“…Van den Bulcke et al 75 found that regression models performed better than C4.5 decision trees in identifying cases of modeling of medium-chain acyl-CoA dehydrogenase deficiency. They observed that decision trees were less robust, with changes in variable selection choices.…”

Diagnosis support systems for rare diseases: a scoping review

“…The number of patients could vary from less than 20 patients [20,21,24,35,40,43,55] to more than 1000 patients [37,47,51,62,72,75]. As expected, the number of patients was usually more important for publications targeting more than one disease (groups 2 and 3) than for publications targeting one specific rare disease (group 1) ( Table 2).…”

“…Associations of different algorithms, such as fusion algorithms, were encountered in 2 studies [52,53]. Other authors reported using more traditional statistical modeling, such as regression [24] or decision trees [42,60]. The K Nearest Neighbors algorithm was used in 2 studies [51,72].…”

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health

“…Researchers have experimented with several ML models including probability [61, 70, 71], decision tree [58, 72, 73], discriminant [54, 70] and other types of ML models [52, 74, 75] to build phenotype categorization systems. In each case, the models essentially approach phenotyping as a classification or categorization problem with a positive class (the target phenotype) and a negative class (everything else).…”