Management Information system for hemoparasitosis detection in canines through machine learning techniques

This study presents the development and implementation of a web-based and mobile Management Information System (MIS) for the early detection and diagnosis of hemoparasitosis in canines, using machine learning algorithms.The system was deployed at Animal Place, a veterinary clinic in Guayaquil, in response to the increasing number of clinical cases associated with hemoparasitic infections.The platform integrates multiple clinical management modules, including a dedicated diagnostic module powered by a decision tree classification algorithm (J48), which achieved an accuracy of 96.4% based on a training set of 746 symptomatological records.The mobile application complements the web system by allowing users to monitor their pets' clinical data and interact with an integrated chatbot assistant developed with DialogFlow.For system development, the Extreme Programming (XP) methodology was employed to ensure iterative progress, client feedback, and continuous testing.In parallel, the Knowledge Discovery in Databases (KDD) methodology was used to structure and preprocess clinical data, enabling the identification of key variables relevant to hemoparasitosis and training of the predictive model.Usability testing was conducted with 10 participants, including veterinary staff and administrative personnel.Results showed high levels of satisfaction (mean score = 4.5 on a 5-point scale), particularly regarding system functionality, ease of use, and interface clarity.These findings highlight the system's relevance, practicality, and user acceptance in a clinical setting.In conclusion, the proposed MIS successfully automates critical veterinary processes such as client registration, clinical history management, and disease detection, while significantly enhancing the accuracy and efficiency of early hemoparasitosis diagnosis in dogs.This research points out the transformative effects of machine learning and intelligent digital tools in veterinary practice and provides a scalable model for similar applications in animal health.