Comparison of Machine Learning Classification Algorithms and Application to the Framingham Heart Study

arXiv:2402.15005v1 Announce Type: new
Abstract: The use of machine learning algorithms in healthcare can amplify social injustices and health inequities. While the exacerbation of biases can occur and compound during the problem selection, data collection, and outcome definition, this research pertains to some generalizability impediments that occur during the development and the post-deployment of machine learning classification algorithms. Using the Framingham coronary heart disease data as a case study, we show how to effectively select a probability cutoff to convert …

abstract algorithms amplify application arxiv biases classification collection comparison cs.lg data data collection definition health healthcare machine machine learning machine learning algorithms research social stat.ml study type