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<p>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 &lt; 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 &lt; 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 &lt; 0.02), type of breed (HR = 0.344, p- value &lt; 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.</p>

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Survival analysis and prediction of cow under mastitis disease in Rwanda using K-nearest neighbors: a retrospective study

  • 期刊论文

  • 发表日期：

    2026年4月7日

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  • Maximillian Manzi,
  • Method Gasana,
  • Fabrice Ndayisenga,
  • Jacques Munyemana,
  • Florien Nkurunziza,
  • Eugene Niyonzima

摘要

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.