Ballesteros-Ricaurte Javier Antonio, Fabregat Ramon, Carrillo-Ramos Angela, Parra Carlos, Moreno Andrés
Escuela de Ingeniería de Sistemas y Computación, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia.
Doctorado en Ingeniería, Pontificia Universidad Javeriana, Bogotá 110231, Colombia.
Math Biosci Eng. 2025 Apr 8;22(5):1140-1158. doi: 10.3934/mbe.2025041.
The prediction of bovine infectious diseases is a constant challenge as generally, only laboratory data is available not allowing the study of their relationship with each disease's risk factors. The diseases neosporosis and bovine viral diarrhea, which are present in Colombia, the United States, Mexico, Brazil, and Argentina, cause reproductive problems in cattle and generate economic losses for ranchers. Although there are mathematical models that can evaluate which cattle are susceptible to these diseases, these provide limited information, maintaining the need for a model that provides information on both transmission and mechanisms for controlling the disease. In this article, a machine learning model is presented that combines laboratory data with risk factors in a neural network to predict the presence of bovine neosporosis. The proposed model was implemented with data from previous studies conducted in the municipality of Sotaquirá, Boyacá, Colombia, and obtained an accuracy of 94% in predicting the presence of the disease. It can be concluded that incorporating laboratory data into machine learning algorithms improves the prediction of the presence of these diseases. Furthermore, the proposed system not only predicts but also provides useful information for clinical decision-making, making it a valuable tool in the veterinary field.
牛传染病的预测一直是一项挑战，因为一般来说，只有实验室数据可用，无法研究其与每种疾病风险因素的关系。新孢子虫病和牛病毒性腹泻这两种疾病在哥伦比亚、美国、墨西哥、巴西和阿根廷都有出现，会导致牛的繁殖问题，并给牧场主造成经济损失。虽然有数学模型可以评估哪些牛易患这些疾病，但这些模型提供的信息有限，因此仍需要一个能提供疾病传播和控制机制信息的模型。本文提出了一种机器学习模型，该模型将实验室数据与风险因素结合到神经网络中，以预测牛新孢子虫病的存在。所提出的模型是利用在哥伦比亚博亚卡省索塔基拉市进行的先前研究的数据实施的，在预测疾病存在方面获得了94%的准确率。可以得出结论，将实验室数据纳入机器学习算法可改善对这些疾病存在情况的预测。此外，所提出的系统不仅能进行预测，还能为临床决策提供有用信息，使其成为兽医领域的一个有价值的工具。
