## 4 Conclusions  
The challenges in adopting the integrated data support system are vast and multifactorial, which include the complex utilization of the massive flow of different and non-uniform data. In this study, it was demonstrated that a data-mining hub that includes only relevant information for detecting health conditions was explored. The development of the data-mining hub (ThinkDairy Hub) in this study clearly and concisely defined the target health condition: heat stress and subclinical mastitis to increase the accuracy of the detection. This study has developed a simple but innovative approach (data-mining hub that uses a simple and efficient classifying algorithm: KNN) in which combined information from different independent data was used to accurately detect the health and wellness status of the dairy cows. The practical application of the data-mining hub has shown that using 21 parameters instead of 13 and 8 parameters resulted in the high overall accuracy of detecting heat stress and subclinical mastitis in dairy cows with a high precision effect, reflecting the low percentage of misclassifying the conditions of the dairy cows. Using increased relevant information and combining them using the data-mining hub showed a potential AI-based tool to support decision making in dairy farming. For future studies, different classifying algorithms such as random forest, support vector machine, and artificial neural networks for the identification of the real-time condition of dairy, and modeling and simulation to arrive at the action and feed ratios of the ThinkDairy data-mining hub, should be studied further to prove its application at the farm level as an AI-based tool in accurately analyzing the conditions of cows for precision feeding of the animals. Further studies on the different indicators for different health conditions, such as lameness and acidosis of dairy cows, should also be explored using the data-mining hub. In this article, it can be implied that an artificial intelligence-based tool, such as the proposed theoretical data-mining hub of dairy cows, could maximize the use of continuously generated but underutilized data in farms, thus simplifying the repetitive and difficult decision making tasks in dairy farming.  
Figure 4: Percentage detection of normal, heat stress, and subclinical mastitis using the data-mining hub with the different number of parameters: HI (21 parameters), MID (13 parameters), and LO (8 parameters).  
**Author Contributions:** Concept by [PERSON], [PERSON] and [PERSON]; formal analysis by [PERSON], [PERSON]-i.-[PERSON], [PERSON], [PERSON], [PERSON] and H.K.H.; methodology by [PERSON], [PERSON]-i.C., [PERSON], [PERSON], [PERSON] and H.K.H.; validation by [PERSON]-[PERSON], S.H.C.; writing of the original draft by [PERSON], [PERSON] writing: review and editing by [PERSON], [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Research Foundation (grant number NRF-2018R1D1A3B07048219) and Brain Korea 21 Center for Bio-Health Industry in Korea.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets from this study are available from the corresponding authors upon reasonable request.  
**Conflicts of Interest:** The authors have no potential personal or financial conflict of interest relevant to this article to declare.