Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia

Research Article

Unordered Features Selection of Low Birth WeightDatain Indonesiausing the LASSO and Fused LASSO Techniques

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  • @INPROCEEDINGS{10.4108/eai.2-8-2019.2290344,
        author={Yenni  Kurniawati and Khairil Anwar  Notodiputro and Bagus  Sartono},
        title={Unordered Features Selection of Low Birth WeightDatain Indonesiausing the LASSO and Fused LASSO Techniques},
        proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia},
        publisher={EAI},
        proceedings_a={ICSA},
        year={2020},
        month={1},
        keywords={fused lasso lasso low birth weight penalized regression unordered features},
        doi={10.4108/eai.2-8-2019.2290344}
    }
    
  • Yenni Kurniawati
    Khairil Anwar Notodiputro
    Bagus Sartono
    Year: 2020
    Unordered Features Selection of Low Birth WeightDatain Indonesiausing the LASSO and Fused LASSO Techniques
    ICSA
    EAI
    DOI: 10.4108/eai.2-8-2019.2290344
Yenni Kurniawati1,*, Khairil Anwar Notodiputro2, Bagus Sartono2
  • 1: Departement of Mathematics, Universitas Negeri Padang, Padang, 25171, Indonesia
  • 2: Department of Statistics, IPB University, Bogor, 16680, Indonesia
*Contact email: yenni.mathunp@gmail.com

Abstract

This paper aims to analyze the Low Birth Weight (LBW) data of infants in Indonesia by using the LASSO and Fused LASSO techniques. Fused LASSO is usually used to select parameters for ordered features. In this case, the features are unordered. Therefore, this research adopts three techniques in ordering features. Furthermore, all these Fused LASSO techniques and LASSO are compared. This paper utilizes data on 1,176 LBW infants collected from the 2017 Indonesian Demographic and Health Survey (IDHS). The results showed that LASSO has the sparsest solutionbased on the 5-fold cross-validation. Thefeatures that contribute to LBW are mothers' occupation, mothers' age, antenatal care, multiple birth, and birth order. However, Fused LASSO 1 has the lowest AIC and BIC valuecompared to other ordering techniques.Ordering features using the correlation between the features and the outcomes is recommended as an alternative technique to sort unordered features.