Web: http://arxiv.org/abs/2209.06947

Sept. 16, 2022, 1:11 a.m. | Charles B. Delahunt, Noni Gachuhi, Matthew P. Horning

cs.LG updates on arXiv.org arxiv.org

Communal hill-climbing, via comparison of algorithm performances, can greatly
accelerate ML research. However, it requires task-relevant metrics. For
diseases involving parasite loads, e.g., malaria and neglected tropical
diseases (NTDs) such as schistosomiasis, the metrics currently reported in ML
papers (e.g., AUC, F1 score) are ill-suited to the clinical task. As a result,
the hill-climbing system is not enabling progress towards solutions that
address these dire illnesses. Drawing on examples from malaria and NTDs, this
paper highlights two gaps in current …

arxiv case diseases machine machine learning metrics

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