Application of CHAID Algorithm for the Identification of Morphological Traits of Indigenous Sheep Body Weight

Significant traits of body weight by CHAID Analysis.

Authors

  • Muhammad Arsalan Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan
  • Muhammad Aman Ullah Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan
  • Abdul Waheed Department of Livestock and Poultry Production, Bahauddin Zakariya University, Multan, Pakistan

Keywords:

Body Weight, CHAID, Morphological Trait, Predictor, Indigenous Sheep, Southern Punjab, Pakistan

Abstract

The objective of this research study was the identification of significant morphological traits to predict the live body weight of indigenous sheep of southern Punjab, Pakistan. Application of Chi-Square Automatic Interaction Detection (CHAID) was used to achieve the objective of the current study. Pearson correlation technique was used to see the relationship of morphological traits with live body weight. The data of 13 morphological traits of 291 indigenous sheep was used using a simple random sample technique. The morphological traits such as barrel depth, body length, ear length, ear width, head length, head width, heart girth, neck length, neck width, rump length, rump width, tail length, and withers height were used. The data consists of 130 rams and 161 eves. The dependent variable was the live body weight of indigenous sheep. The barrel depth, rump width, and heart girth were strongly correlated with live bodyweight having correlations 0.968, 0.936, and 0.925 respectively. The result of CHAID analysis showed that barrel depth and heart girth are significant predictors of live body weight (p-value<0.001).

References

M.A. Khan., M.M. Tariq, E. Eyduran, A. Tatliyer, M. Rafeeq, F. Abbas, N. Rashid, M.A. Awan, and K. Javed. Estimating body weight from several body measurements in Harnai sheep without a multicollinearity problem. The Journal of Animal & Plant Sciences, 24(1): 2014, Page: 120-126. ISSN: 1018-7081 (2014).

M. Ali., E. Eyduran, M.M. Tariq, C. Tirink, F. Abbas, M. A. Bajwa, M. H. Baloch, A. H. Nizamani, A. Waheed, M. A. Awan, S. H. Shah, Z. Ahmad, and S. Jan. Comparison of Artificial Neural Network and Decision Tree Algorithms used for Predicting Live Weight at Post Weaning Period from Some Biometrical Characteristics in Harnai Sheep. Pakistan Journal of Zoology. 47(6) 1579-1585 (2015).

M. Olfaz, C. Tirink, and H. Önder. Use of CART and CHAID Algorithms in Karayaka Sheep Breeding. Kafkas Univ Vet Fak Derg 25 (1): 105-110 (2019).

E. Eyduran., K. Karakus, S. Keskin, and F. Cengiz. Determination of Factors Influencing Birth Weight Using Regression Tree (RT) Method. Journal of Applied Animal Research, 34:2, 109-112 (2008).

M.T. Mohammad., M. Rafeeq, M. A. Bajwa, M. A. Awan, F. Abbas, A. Waheed, F. A. Bukhari, and P. Akhtar. Prediction of Body Weight from Body Measurements Using Regression Tree (RT) Method for Indigenous Sheep Breeds in Balochistan, Pakistan. The Journal of Animal & Plant Sciences, 22(1): 2012, Page: 20-24. ISSN: 1018 – 7081. (2012).

A. Yakubu., A. D. Awuje, and J. N. Omeje. Comparison of multivariate logistic regression and classification Tree to assess factors influencing the prevalence of abortion in Nigerian cattle breeds. The Journal of Animal & Plant Sciences, 25(6): 2015, Page: 1520-1526 (2015).

T. Fehér. Using Regression Trees in Predictive Modeling. Production Systems and Information Engineering Volume 4 (2006), pp. 115-124 (2006).

E. Önder, and Ş. Uyar. CHAID Analysis to Determine Socioeconomic Variables that Explain Students' Academic Success. Universal Journal of Educational Research 5(4): 608-619, (2017).

Y.B. Yücel. Determination of factors affecting happiness level by Classification tree technique. European Journal of Business and Social Sciences. 6(2) 54-62 (2017).

M. Milanović, and M. Stamenković. CHAID Decision Tree: Methodological Frame and Application. Economic Themes (2016) 54(4): 563-586 (2016).

Koc, Y., E. Eyduran, and O. Akbulut. Application of Regression Tree Method for Different Data from Animal Science. Pakistan Journal of Zoology. 49(2) 599-607 (2017).

Y.Y. Song, and Y. Lu. Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry. 27(2) 130-135 (2015).

G.V. Kass. An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics, 29(2) 119-127 (1980).

G. Ritschard. CHAID and Earlier Supervised Tree Methods. In J.J. McArdle & G. Ritschard (eds), Contemporary Issues in Exploratory Data Mining in Behavioral Sciences, Routledge, New York, pages 48-74 (2013).

H. Camdeviren., M. Mendes, M.M. Ozkan, F. Toros, T. Sasmaz, and S. Oner. Determination of depression risk factors in children and adolescents by regression tree methodology. Acta Med. Okayama, 2005 Vol. 59, No. 1, pp. 19-26 (2005).

Pakistan Economic Survey (2018-19) http://www.finance.gov.pk/survey/chapters_19/2-Agriculture.pdf.

M. Qasim, M. Amin, M. N. Akram, T. Omer, and F. Hussain, Forecasting Buffalo Population of Pakistan using Autoregressive Integrated Moving Average (ARIMA) Time Series Models. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 56(3), 27-36 (2019).

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Published

2021-03-09

How to Cite

Arsalan, M., Aman Ullah, M., & Waheed, A. (2021). Application of CHAID Algorithm for the Identification of Morphological Traits of Indigenous Sheep Body Weight: Significant traits of body weight by CHAID Analysis. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 57(2), 75–80. Retrieved from http://ppaspk.org/index.php/PPAS-A/article/view/25

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