investigating machine learning based applications in prediction small animal diseases utilizing laboratory parameters

Machine learning (ml) techniques are gaining widespread application in solving predictive tasks in veterinary medicine and have emerged as promising tools for disease characterization and diagnosis. by analyzing large datasets encompassing clinical and laboratory parameters, ml algorithms can identify complex patterns and associations indicative of disease states. biomarkers, such as leukocyte count, c-reactive protein (crp), and procalcitonin (pct), have been commonly used to predict the occurrence of life-threatening septicemia and provide prognostic information, given the need for prompt intervention. however, such diagnosis methods require much time and money. therefore, it has been proposed a method with a high prediction capability using machine learning (ml) models based on complete blood count (cbc) and differential leukocyte count (dc) and compare its performance with traditional crp or pct biomarker methods and those of models incorporating crp or pct biomarkers. using machine learning algorithms, an advanced, easy-to-use sensor was developed for rapidly alerting farmers as to low red blood cell count of their animals. this innovation can decrease the time and skill often needed for such evaluations but also establishes the basis for a straightforward, efficient, and easy-to-use technique of screening for anemia. studies have utilized various ml algorithms, including random forests, support vector machines, and neural networks, to detect patterns indicative of infections for the diagnosis of various diseases such as leptospirosis.infectious diseases can cause death in complicated situations. ml algorithms can assist in the diagnosis of infectious diseases at early stages. artificial intelligence has the potential to enhance the care and handling of livestock in agricultural environments it is suggested that more extensive and additional activities in this field be by researchers.