# Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows
## 1 Introduction  
### Background  
With the quick advancement of contemporary animal husbandry, dairy farming has become one of the most important industries in the world. High-quality milk is of great significance to both human health and the economy of the dairy industry. In dairy farming, cows can develop various health conditions due to factors such as environmental conditions, feed quality, and exposure to pathogens. Common health issues include mastitis, pneumonia, and hoof inflammation [1]. One of the biggest issues facing dairy producers is mastitis, which not only affects milk production and composition, but also affects the health of dairy cows and causes huge economic losses [2]. In India alone, mastitis causes an economic loss of about 237 million rupees per year, of which subclinical mastitis accountsfor about 70% [3]. According to [PERSON] [4], on average, 50% of cows in herds without a mastitis health control program have subclinical mastitis. Clinical mastitis is thought to be responsible for 20-30% of the economic losses related to mastitis, with subclinical mastitis accounting for the remaining portion. In 90-95% of cases, despite the normal appearance of both the udder and milk, elevated somatic cell count (SCC) is accompanied by changes in milk composition and reduced yield. Chronical mastitis has been found to slow down normal calf development [5]. Therefore, early detection and treatment of mastitis in dairy cows is important for maintaining cow health, ensuring dairy product quality, improving production efficiency and animal welfare, and reducing financial losses for farms. [PERSON] et al. [6] employed support vector machine technology to identify dairy cows with subclinical and clinical mastitis according to their somatic cell counts. The cows were classified as healthy or infected. Somatic cell count (SCC) is a critical tool for diagnosing subclinical mastitis in dairy cows, yet its accuracy can be influenced by various non-infectious factors, leading to false-positive outcomes. These factors encompass the stage of lactation, environmental stress, milk yield levels, and genetic predispositions, which can cause an abnormal increase in SCC, thus generating false-positive results [7]. Consequently, when interpreting SCC data, it is imperative to consider these non-infectious factors comprehensively to prevent misdiagnosis and unnecessary treatment. In order to predict the presence of subclinical mastitis in Italian Mediterranean water buffalo, [PERSON] et al. [8] used machine learning (ML) analysis. They built prediction models using four different algorithms: neural networks, random forests, support vector machines, and generalized linear models. Based on multiple evaluation criteria, the test set was best predicted by the neural network, and it was established that machine learning techniques have promise for enhancing the monitoring and prevention of subclinical mastitis. The advent of these emerging methods offers fresh approaches to the early identification of mastitis in dairy cows and is anticipated to establish itself as a standard detection technique in dairy farming in the future. [PERSON] et al. [9] presented an enhanced mothflapping optimization algorithm (IMFO) and tested it on the UCI dataset and the dairy cow subclinical mastitis disease dataset. Comparative analysis revealed IMFO's superior feature selection performance, which enables more efficient and accurate detection of subclinical mastitis in large dairy herds. [PERSON] et al. [10] compared the accuracy of certain machine learning algorithms, such as logistic regression, support vector machines, Gaussian naive Bayes, k-nearest neighbors, and decision trees, with ensemble learning techniques, such as bagging, boosting, stacking, and super learner models. The results of the study confirmed the beneficial effect of the integrated ML method. Existing machine learning models have significantly advanced in their ability to forecast the likelihood of subclinical mastitis in dairy cows; however, they still exhibit limitations.