## 3 Results and Discussion
### Practical Application of the Data-Mining Hub  
The overall accuracy and Kappa coefficient obtained by KNN in detecting heat stress and subclinical mastitis based on three different parameters are shown in Table 2. The highest overall classification accuracy in detecting heat stress and subclinical mastitis was observed in the group with the highest data parameters used (HI: 21) at 94.5% and 93.2% accuracy, respectively. This finding signifies that the data-mining hub that uses all the selected parameters identified and discussed in the above section could accurately detect heat stress and subclinical mastitis in dairy cows. The overall classification accuracy of heat stress in MID:13 and LO:8 groups were 88.6% and 84.8%, respectively, and 87.9% and 83.7% for subclinical mastitis, respectively. The MID:13 group, which includes the environmental condition, body temperature, standing, and lying behaviors, milk yield, and feed intake, could detect heat stress and subclinical mastitis at a moderately high accuracy level. The LO:8 group, which consisted of the environmental condition, milk yield, and feed intake parameters, resulted in moderately high accuracy detection of heat stress (84.8%), signifying that detecting heat stress in dairy cows could be based mainly on a few numbers of parameters. However, using a high number of parameters will result in the highest accuracy in detecting heat stress in dairy cows. The overall accuracy of detecting heat stress and subclinical mastitis increased with the increasing number of data parameters, signifying that increased use of information could result in more accurate detection of health conditions in dairy cows. The Kappa coefficient of HI:21 and MID:13 groups showed complete consistency with the true values of the data (>0.81), while the LO:8 group showed a high degree of consistency (0.79-0.80). Figure 3: Pearson correlation of the parameters with the health condition of dairy cows. In order to study the effect of classification, the KNN algorithm was also used to obtain the precision of classifying the condition of the cows, as shown in Table 3. Among the three groups, only the data-mining hub using a high number of parameters (HI:21) reach more than 90% precision. The precision of recognition for the normal condition was at 92.8% and 93.7% for heat stress conditions in the HI:221 group, which reflected that the data-mining hub using the high number of data parameters could hardly misclassify the two different conditions. The same observation was noted in the effect of classification in detecting subclinical mastitis, normal condition, and others. The precision of recognition for normal condition and subclinical mastitis in the HI:21 group were 93.7% and 94.2%, respectively. This finding reflected that using the high number of data parameters will not easily misclassify the subclinical mastitis, normal, or other conditions of the dairy cows. When the data-mining hub was used to detect normal and heat stress conditions in the same datasets of the dairy cows, the HI:21 group detected that 43.75% of the cows were heat-stressed, while in the MID:13 and LO:8 groups, 59.38% and 71.88% were detected as heat-stressed cows, as shown in Figure 4. The result of the practical application of the data-mining hub signifies that using a low number of parameters in detecting heat stress in dairy cows could overly detect heat stress conditions in dairy cows. The high percentage detection of heat-stressed cows (71.88%) using the LO:8 group could mean that the data-mining hub with the lower number of parameters over-detected dairy cows that are in normal condition and incorrectly classify them as being in a heat-stressed state. As evidenced in the precision of classification, the LO:8 group showed an 80.2% precision, indicating that there is an almost 20% percentage of misclassifying the dairy cows in heat stress.