Deciphering Foot and Mouth Disease Predictive Modeling: Uncovering Attribute Correlations and Risk Factors with Advanced Machine Learning
Deciphering Foot and Mouth Disease Predictive Modeling: Uncovering Attribute Correlations and Risk Factors with Advanced Machine Learning
Mokammel Hossain Tito, Most Hoor E Jannat, Marzia Afrose, S.M. Jubayer Ahmed, Shah Md Maruf, Md. Arafat Hossain, Safiullah Samani, Ruksana Jahan Mira, Barshon Saha, Asraful Islam Jihad and Tonmoy Kumar Das
ABSTRACT
Foot and mouth disease (FMD) presents a formidable challenge to the global livestock industry, with significant implications for food security, trade, and animal welfare. Despite advancements, FMD remains endemic, with a profound impact on economies worldwide. This study, conducted in Ethiopia’s East Wollega zone, we utilized a dataset comprising 266 bovine sera samples collected from Diga, Guto Gida, and Nekemte districts, employed machine learning (ML) algorithms to predict FMD outbreaks and assess attribute correlations. The dataset is taken from mendely with prevalence ranges from 4.8% to 72.1% in cattle. In this paper we have used total of five algorithms including Naïve Bayes, MLP (Multilayer Perceptron), SMO (Sequential Minimal Optimization), AdaBoostM1, and REP Tree. Each model evaluated using various criteria, such as Accuracy, Kappa statistic, Precision, Recall, F measure, Matthews Correlation Coefficient (MCC) and required time to perform the model. Analysis revealed Multilayer Perceptron (MLP) as the most effective model based on various evaluation criteria, achieving an impressive accuracy of 82.21%. Attributes were ranked by importance, with Age, Body Condition, and Physiology identified as the top critical factors. Moreover, eight association rules were derived, shedding light on attribute correlations in FMD occurrence. Our findings underscore the potential of ML in disease prediction and contribute valuable insights for proactive disease management strategies, offering a pathway towards safeguarding livestock and ensuring sustainable agricultural practices.
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