Precision dairy monitoring technologies provide real-time monitoring of cow health, reproduction, and management. The objective of this study was to assess disease detection accuracy by precision dairy technology health alerts and machine-learning techniques. We hypothesized the technology would detect health events, machine-learning would improve disease detection, and incorporating cow history with behavior changes would improve disease detection. The study occurred from October 2015 to October 2016 on 4 Kentucky dairy farms (1,374 cows). Health alerts were generated based on changes in eating (h/d), lying (h/d), standing (h/d), walking (h/d), and activity (steps/d). The FREQ procedure of SAS 9.4 was used to identify true positives, true negatives, false positives, and false negatives based on technology-generated cow alerts and recorded disease events by the lead researcher and dairy producers. Sensitivity, specificity, accuracy, and balanced accuracy were also calculated. Daily information collected by the technology (eating time, lying time, standing time, walking time, and activity) and cow history were incorporated into machine-learning prediction models. Technology-generated health alert sensitivity remained between 13 and 48% with a 91 to 97% specificity. Maximum balanced accuracy achieved with technology-generated health alerts was 59% at the widest time-windows of 5 d before to 2 d after and 3 d before to 3 d after the day of disease detection. The greatest balanced accuracy occurred when all behavior changes were considered in combination predicting any possible disease instead of a specific disease. All machine-learning analyses performed similarly and improved sensitivity and balanced accuracy compared with the technology-generated health alerts. Sensitivity ranged from 67 to 90%, specificity from 42 to 87%, accuracy 67 to 99%, and balanced accuracy from 66 to 87% across time-windows, disease categories, and behavior combinations. Unlike the technology-generated alerts, machine-learning predictions were best during the 24 h before the day of disease identification and when individual diseases were predicted.