Estimating Fish Body Weight from Morphological Features Using Some Data Mining Algorithms.
7 affiliations
- 1Biometry and Genetics Unit, Department of Animal Science, Faculty of Agriculture, Bingöl University, Bingöl 12000, Türkiye.
- 2Department of Animal Science, Centre for Sustainable Agriculture and Rural Development, Faculty of Agriculture, Nasarawa State University, Shabu-Lafia Campus, Lafia 950101, Nigeria.
- 3Department of Hydrobiology and Ichthyology, Faculty of Animal Science and Water Bioresources, National University of Life and Environmental Sciences of Ukraine, Horikhuvats'kyy Shlyakh 19, 03041 Kyiv, Ukraine.
- 4Department of Aquculture, Faculty of Animal Science and Water Bioresources, National University of Life and Environmental Sciences of Ukraine, Horikhuvats'kyy Shlyakh 19, 03041 Kyiv, Ukraine.
- 5Department of Applied Biology, Animal Breeding and Genetics, Faculty of Animal Science and Water Bioresources, National University of Life and Environmental Sciences of Ukraine, Horikhuvats'kyy Shlyakh 19, 03041 Kyiv, Ukraine.
- 6Department of Animal Technology, Faculty of Animal Science and Water Bioresources, National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony St, 12A, 03041 Kyiv, Ukraine.
- 7Department of Molecular Sciences, Swedish University of Agricultural Sciences, P.O. BOX 7015, 750 07 Uppsala, Sweden.
Abstract
The objective was to compare the performance of various data mining algorithms in estimating the body weight of fish. The effects of age, farm type and body morphological characteristics on the body weight of the hybrid silver carp (Hypophthalmichthys spp.) were analyzed using some data mining methods. The body weight of the fish was predicted using different morphological measurements taken during its lifetime and after slaughter. Prediction performances of the Chi-Squared Automatic Interaction Detector (CHAID), Classification and Regression Trees (CART), Artificial Neural Networks (ANN), Random Forest (RF), and Multivariate Adaptive Regression Splines (MARS) algorithms were compared. The prediction capabilities of the fitted models were assessed using model fit statistics. The MARS algorithm was demonstrated to be the most effective model for characterizing body weight. Body length showed the greatest relative importance among all traits measured during the fish's lifetime. When the body length of fish exceeds 145 mm, the body weight is expected to reach 173 g. The MARS algorithm is the most effective model for predicting the body weight of fish, based on the experimental data. It provides an excellent alternative to existing data mining techniques.
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BibTeX
@article{celik2026estimating,
title = {{Estimating Fish Body Weight from Morphological Features Using Some Data Mining Algorithms.}},
author = {Şenol Çelik and Abdulmojeed Yakubu and Alina Makarenko and Ruslan Kononenko and Iryna Kononenko and Andriy Getya and Mykhailo Matvieiev and Nataliia Hryshchenko and Galia Zamaratskaia},
journal = {Animals : an open access journal from MDPI},
year = {2026},
month = oct,
doi = {10.3390/ani16193124},
url = {https://europepmc.org/article/MED/42883093},
note = {CC BY},
}
RIS
TY - JOUR
TI - Estimating Fish Body Weight from Morphological Features Using Some Data Mining Algorithms.
AU - Şenol Çelik
AU - Abdulmojeed Yakubu
AU - Alina Makarenko
AU - Ruslan Kononenko
AU - Iryna Kononenko
AU - Andriy Getya
AU - Mykhailo Matvieiev
AU - Nataliia Hryshchenko
AU - Galia Zamaratskaia
PY - 2026
DA - 2026/10/05
JO - Animals : an open access journal from MDPI
DO - 10.3390/ani16193124
UR - https://europepmc.org/article/MED/42883093
AB - The objective was to compare the performance of various data mining algorithms in estimating the body weight of fish. The effects of age, farm type and body morphological characteristics on the body weight of the hybrid silver carp (Hypophthalmichthys spp.) were analyzed using some data mining methods. The body weight of the fish was predicted using different morphological measurements taken during its lifetime and after slaughter. Prediction performances of the Chi-Squared Automatic Interaction Detector (CHAID), Classification and Regression Trees (CART), Artificial Neural Networks (ANN), Random Forest (RF), and Multivariate Adaptive Regression Splines (MARS) algorithms were compared. The prediction capabilities of the fitted models were assessed using model fit statistics. The MARS algorithm was demonstrated to be the most effective model for characterizing body weight. Body length showed the greatest relative importance among all traits measured during the fish's lifetime. When the body length of fish exceeds 145 mm, the body weight is expected to reach 173 g. The MARS algorithm is the most effective model for predicting the body weight of fish, based on the experimental data. It provides an excellent alternative to existing data mining techniques.
SN - 2076-2615
ER -