## 1 Introduction  
Dairy farming is a decision-intensive field of agriculture that must rely on a holistic system approach accounting for the well-being, physiological, behavioral, and health conditions of the animal to meet the specific requirements of the dairy cows. Its management requires cooperation and close communication between dairy experts, farm managers, and dairy factories. The birth of technological innovations, such as the use of the Internet of Things, has helped in detecting and identifying the condition of the cows and has contributed to the betterment of the welfare of the animals. State-of-the-art measures in dairy farming that enable feed intake measurements include acoustic monitoring [1; 2; 3], chewing activity, or jaw movement augmented with sensor data [4], noseband pressure sensors [5], and electromyography [6]. In automated health diagnostics, which are commercially and widely available [7; 8], the movement, behavior, and physiological conditions of dairy cows are used to relay information about their well-being and health, such as MooMonitor [9] and Zigbee [10]. The use of these advanced monitoring and diagnostic tools, such as applications and expert systems, are normally restricted between the user and the proprietary of the tool, and these applications mostly focus on one problem and do not involve data integration [11]. Analyzing independent and disparate data could be informative and could create isolated judgments about the animals, and hence, drive the dairy farmers in making decisions based on intuition and experiences rather than based on the unique requirements of the cows. A revolution in the era of artificial intelligence and big data has tried to resolve this by making use of the massive volumes of widely varied data that can be taken, analyzed, and interpreted [12], and integrated to produce a more insightful outcome. Some of the dairy farm decision system tools include, but are not limited to, the Dairy Brain [13], My Dairy Dashboard [14], and Cargill Dairy EntelligenTM [15]. However, the practical applications and adoption of these efforts are not sustained in dairy farms, and in fact, only 22 peer-reviewed papers have reported data integration in decision making in dairy farming [16]. Taking full advantage of the opportunities of the created data has been difficult for dairy farmers and animal scientists alike to accurately identify the condition of the cows for efficiently maximizing the productivity of the cows.  
Hence, this study aimed to show the possibility of developing an artificial-based tool that integrates relevant information (parameters or traits) needed for accurately identifying the condition or performance of the cow. Specifically, the objectives of the study were (1) to demonstrate a comprehensive theoretical framework of the use of a data-mining hub in improving the precision of identifying the condition of dairy cows; (2) to provide a list of parameters or traits of the dairy cows that are relevant in detecting conditions of cows, and (3) to conduct a practical application to compare the number of traits or parameters needed to identify the statuses of the specific cow, which in this study, are identified as a normal condition, heat-stress, and subclinical mastitis risk (SCMR).