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Feature Selection Based on Ranking Metagenomic Relative Abundance for Inflammatory Bowel Disease Prediction

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Complex, Intelligent and Software Intensive Systems (CISIS 2024)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 87))

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Abstract

Inflammatory bowel diseases can be severe, but with access to metagenomic data, we can diagnose them and take the necessary steps to prevent further complications. The key to identifying the composition in the human body that causes the disease is carefully selecting features from the metagenomic data. Our research has demonstrated that using the Random Forest machine learning technique to rank the relative abundance of features for disease prediction tasks is reliable. We have also discovered that selecting features ranging from 1 to 50 improves the accuracy of diagnosis. In addition, we have performed an intersection on the Top 10, 20, 30, 40, and 50 features to determine which ones appear in all datasets. Our experiments on six inflammatory bowel disease-related datasets have yielded better results than previous studies.

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Notes

  1. 1.

    https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html.

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Authors and Affiliations

  1. Can Tho University, Can Tho, Vietnam

    Hien Thanh Thi Nguyen, Hat Nguyen Le & Hai Thanh Nguyen

Authors
  1. Hien Thanh Thi Nguyen
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  2. Hat Nguyen Le
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  3. Hai Thanh Nguyen
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Correspondence to Hai Thanh Nguyen .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Nguyen, H.T.T., Le, H.N., Nguyen, H.T. (2024). Feature Selection Based on Ranking Metagenomic Relative Abundance for Inflammatory Bowel Disease Prediction. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 87. Springer, Cham. https://doi.org/10.1007/978-3-031-70011-8_9

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