Machine Learning Based Detection of Chronic Kidney Disease using Full Blood Count from a Ghanaian Hospital Population with an Integrated Web Application

Chronic kidney disease (CKD) is usually silent until advanced, and the tests confirming it are frequently unaffordable in sub-Saharan Africa, whereas the full blood count (FBC) is inexpensive and widely performed. We assessed whether machine learning applied to the FBC can detect CKD, and how far its reported performance depends on where synthetic minority oversampling (SMOTE) is applied. Records of 155 patients at the Ho Technical University Hospital, Ghana, 43 (27.7%) with CKD, yielded ten features: age, cardiovascular disease and eight raw FBC parameters. Eleven algorithms, six resampling s

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