Screening PubMed abstracts: is class imbalance always a challenge to machine learning?

• Main Authors: Corrado Lanera, Paola Berchialla, Abhinav Sharma, Clara Minto, Dario Gregori, Ileana Baldi
• Format: Article
• Language: English
• Published: BMC 2019-12-01
• Series: Systematic Reviews
• Subjects: Classification; Indexed search engine; Machine learning; Text mining; Unbalanced data, systematic review
• Online Access: https://doi.org/10.1186/s13643-019-1245-8

Abstract Background The growing number of medical literature and textual data in online repositories led to an exponential increase in the workload of researchers involved in citation screening for systematic reviews. This work aims to combine machine learning techniques and data preprocessing for class imbalance to identify the outperforming strategy to screen articles in PubMed for inclusion in systematic reviews. Methods We trained four binary text classifiers (support vector machines, k-nearest neighbor, random forest, and elastic-net regularized generalized linear models) in combination with four techniques for class imbalance: random undersampling and oversampling with 50:50 and 35:65 positive to negative class ratios and none as a benchmark. We used textual data of 14 systematic reviews as case studies. Difference between cross-validated area under the receiver operating characteristic curve (AUC-ROC) for machine learning techniques with and without preprocessing (delta AUC) was estimated within each systematic review, separately for each classifier. Meta-analytic fixed-effect models were used to pool delta AUCs separately by classifier and strategy. Results Cross-validated AUC-ROC for machine learning techniques (excluding k-nearest neighbor) without preprocessing was prevalently above 90%. Except for k-nearest neighbor, machine learning techniques achieved the best improvement in conjunction with random oversampling 50:50 and random undersampling 35:65. Conclusions Resampling techniques slightly improved the performance of the investigated machine learning techniques. From a computational perspective, random undersampling 35:65 may be preferred.