Prediction of marbling score and carcass traits in Korean Hanwoo beef cattle using machine learning methods and synthetic minority oversampling technique

Pricing of Hanwoo beef in the Korean market is primarily based on meat quality, and particularly on marbling score. The ability to accurately predict marbling score early in the life of an animal is extremely valuable for producers to meet the requirements of their target market, and for genetic selection. A total of 3989 Korean Hanwoo cattle (2108 with 50 k SNP genotypes) and 45 phenotypic features were available for this study. Four machine learning (ML) algorithms were applied to predict six carcass traits and compared against linear regression prediction models. In most scenarios, SMO was the best performing algorithm. The most and least accurately predicted traits were carcass weight and marbling score with correlation of 0.95 and 0.64 respectively. Additionally, the value of using a synthetic minority over-sampling technique (SMOTE) was evaluated and results showed a slight improvement in the prediction error of marbling score. Machine Learning approaches can be useful tools to predict important carcass traits in beef cattle. •Pricing of Hanwoo beef in the prime beef market is primarily based on meat quality specifically on marbling score.•Machine learning approaches were successfully employed to predict Marbling Score and other carcass traits in Hanwoo cattle.•In most cases, support vector machine with SMO, was the best performing algorithm in prediction of carcass traits.•Application of synthetic minority over-sampling technique (SMOTE) on improvement of the prediction error is promising.•Prediction methods introduced in this paper can be effectively utilized to enhance Hanwoo breeding programs.