Identification of parameters and formulation of a statistical and machine learning model to identify Babesia canis infections in dogs using available ADVIA hematology analyzer data

Abstract

Background Canine babesiosis is an important tick-borne disease in endemic regions. One of the relevant subspecies in Europe is Babesia canis, and it can cause severe clinical signs such as hemolytic anemia. Apart from acute clinical symptoms dogs can also have a more chronic disease development or be asymptomatic carriers. Our objective was to identify readily available ADVIA hematology analyzer parameters suggestive of B. canis parasitemia in dogs and to formulate a predictive model. Methods A historical dataset of complete blood count data from an ADVIA hematology system with blood smear or PCR confirmed parasitemia cases was used to obtain a model by conventional statistics (CS) methods and machine learning (ML) using logistical regression and tree methods. Results Both methods identified that important parameters were platelet count, mean platelet volume and percentage large unstained cells. We were able to formulate a CS model and ML model to screen for Babesia parasitemia in dogs with a sensitivity of 84.6% (CS) and 100% (ML), a specificity of 97.7% (CS) and 95.7% (ML) and a positive likelihood ratio (LR+) of 36.78 (CS) and 23.2 (ML). Conclusions This study introduces two methods of screening for B. canis parasitemia on readily available data from ADVIA hematology systems. The algorithms can easily be introduced in laboratories that use these analyzers. When the algorithm marks a sample as ‘suggestive’ for Babesia parasitemia, the sample is approximately 37 times more likely to show Babesia merozoites on blood smear analysis. Graphical Abstract

Prognostic related gene index for predicting survival and immunotherapeutic effect of hepatocellular carcinoma

“…The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for feature selection, and 10 times cross validation was used. [29][30][31]…”

Diagnosis and classification of portosystemic shunts: a machine learning retrospective case-control study

“…Machine learning techniques have been increasingly utilized to enhance diagnostic and predictive capabilities in the veterinary field. Some previous applications of machine learning in veterinary medicine include the diagnosis of hypoadrenocorticism, leptospirosis, and babesiosis in dogs, as well as lameness in cows (16)(17)(18)(19). Predictable patterns in clinicopathologic data have been previously observed in dogs with PSS (6,7,20).…”