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Lower-and-middle income countries are faced with challenges arising from alack of data on cause of death (COD), which can limit decisions on populationhealth and disease management. A verbal autopsy(VA) can provide informationabout a COD in areas without robust death registration systems. A VA consistsof structured data, combining numeric and binary features, and unstructureddata as part of an open-ended narrative text. This study assesses theperformance of various machine learning approaches when analyzing both thestructured and unstructured components of the VA report. The algorithms weretrained and tested via cross-validation in the three settings of binaryfeatures, text features and a combination of binary and text features derivedfrom VA reports from rural South Africa. The results obtained indicatenarrative text features contain valuable information for determining COD andthat a combination of binary and text features improves the automated CODclassification task.  Keywords: Diabetes Mellitus, Verbal Autopsy, Cause of Death, MachineLearning, Natural Language Processing

Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia