Establishing reflex test rules for platelet fluorescent counting method using machine learning models on Sysmex XN‐series hematology analyzer

Abstract Introduction The platelet fluorescent counting (PLT‐F) method is utilized as a reflex test method following the initial test of the platelet impedance counting (PLT‐I) method in clinical practice on the Sysmex XN‐series automated hematology analyzer. Our aim is to establish reflex test rules for the PLT‐F method by combining multiple parameters provided by the “CBC + DIFF” mode of the Sysmex XN‐series automated hematology analyzer. Methods We tested 120 samples to evaluate the baseline bias between the PLT‐F and PLT‐I methods. Then, we selected 1256 samples to establish and test reflex test rules using seven machine learning models (decision Tree, random forest, neural network, logistic regression, k‐nearest neighbor, support vector machine, and Naive Bayes). The training set and test set were divided at a ratio of 7:3. We evaluated the performance of machine learning models on the test set using various metrics to select the most valuable model. Results The PLT‐F method exhibited a high degree of correlation with the PLT‐I method (r = 0.998). The random forest model emerged as the most valuable, boasting an accuracy of 0.893, an area under the curve of 0.954, an F1 score of 0.771, a recall of 0.719, a precision of 0.831, and a specificity of 0.950. The most important variable in the random forest model was mean cell volume, weighted at 15.09%. Conclusion The random forest model, which demonstrated high efficiency in our study, can be used to establish PLT reflex test rules based on the PLT‐F method for the Sysmex XN‐series automated hematology analyzer.