Prediction of Feed Utilization Performance in Clarias gariepinus Using Multiple Linear Regression in Machine Learning

Abstract

Machine learning models can be used to make predictions about nutrient utilization performance index using available proximate analysis data on feed composition. Data from similar experiments on nutrient utilization performance was used to fit a multiple linear regression model for the prediction of four performance indexes. The Specific Growth Rate and percentage inclusion with strength of 0.57 was noted along with a negative relationship between protein efficiency and protein content. A negative relationship between Nitrogen Free Extract (NFE) and Protein Efficiency Ratio (PER) at NFE content ≥25 % was observed. PER was predicted with 85 % accuracy, while Weight Gain (WG), Feed Conversion Ratio (FCR) and Specific Growth Rate (SGR) were predicted at 48 %, 7.6 % and 4.2 % respectively. WG model showed highest coefficient value to ash content (1.23) which is less likely to contribute to fish weight compared to values of fat content (-0.34) and crude protein (-1.02). FCR and SGR models appeared to be dependent on variables outside those included in the proximate analysis data for this study.