ArticleTropical animal health and production2026
Survival analysis and prediction of cow under mastitis disease in Rwanda using K-nearest neighbors: a retrospective study.
Article in Tropical animal health and production, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study investigated the prevalence, risk factors, and predictive modeling of mastitis among 337 lactating cows in Rwanda, revealing a high overall mastitis rate of 73.3%. District-level analysis showed slightly higher prevalence in Nyagatare (74.9%) than Gicumbi (71.4%). Mastitis was influenced by farmer demographics, grazing system, hygiene practices, milking frequency, and calf suckling. Kaplan-Meier survival analysis highlighted longer mastitis-free periods in cows under better management practices. Based on the cox regression results, the study revealed that the sex of farmer (HR = 0.091, p-value = 0.0013), sex of household head (HR = 0.048, p-value < 0.001), age of farmer (HR = 1.987, p- value = 0.0405), category of person who cares (HR = 0.185, p- value = 0.024), grazing system (HR = 3.564, p- value < 0.01), frequency of milking per day (HR = 2.827, p- value = 0.0001), washing hands before milking (HR = 2.387, p- value = 0.0011), hygiene of cow (HR = 0.234, p-value = 0.0003), washing milking area (HR = 0.225, p- value = 0.0448), hygiene of cow (HR = 0.323, p- value < 0.02), type of breed (HR = 0.344, p- value < 0.006), calf suckling (HR = 0.441, p- value = 0.001) and lactating cow size (HR = 0.562, p- value = 0.0178) were found to be significant survival risk factors associated with the prevalence of mastitis disease in Rwanda. Finally, machine learning analysis using the K-Nearest Neighbors (KNN) algorithm found that using three teats as input features yielded optimal predictive performance (accuracy: 0.73, AUC: 0.69). These findings underscore the critical role of hygiene, management, and demographic factors in mastitis control and suggest the potential of machine learning in early detection.
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