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Effect of Feature Discretization on Classification Performance of Explainable Scoring-Based Machine Learning Model

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Computational Science – ICCS 2022 (ICCS 2022)

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Abstract

We improve the utility of the Risk-calibrated Supersparse Linear Integer Model (RiskSLIM). It is a scoring system that is an interpretable machine learning classification model optimized for performance. Scoring systems are commonly used in healthcare and justice. We implement feature discretization (FD) in the hyperparameter optimization process to improve classification performance and refer to the new approach as FD-RiskSLIM. We test the approach using two medical applications. We compare the results of FD-RiskSLIM, RiskSLIM, and other machine learning (ML) models. We demonstrate that scoring models based on RiskSLIM, in addition to being interpretable, perform at least on par with the state-of-the-art ML models such as Gradient Boosting in terms of classification metrics. We show the superiority of FD-RiskSLIM over RiskSLIM.

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Acknowledgements

This work is supported in part by the European Union’s Horizon 2020 research and innovation programme under grant agreement Sano No. 857533 and the International Research Agendas programme of the Foundation for Polish Science, co-financed by the EU under the European Regional Development Fund.

Author information

Authors and Affiliations

  1. Sano Centre for Computational Medicine, Cracow, Poland

    Arkadiusz Pajor & Arkadiusz Sitek

  2. AGH University of Science and Technology, Cracow, Poland

    Arkadiusz Pajor & Bartlomiej Sniezynski

  3. Maria Skłodowska-Curie Memorial Cancer Center, Warsaw, Poland

    Jakub Żołnierek

Authors
  1. Arkadiusz Pajor
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  2. Jakub Żołnierek
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  3. Bartlomiej Sniezynski
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  4. Arkadiusz Sitek
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Corresponding author

Correspondence to Arkadiusz Pajor .

Editor information

Editors and Affiliations

  1. Brunel University London, London, UK

    Derek Groen

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

7 Appendix

7 Appendix

Explanations for the features listed in Fig. 4. G1: binary, from tumor grading, denotes low grade (tumor well differentiated), NEUT > UT: binary, 1 if concentration of neutrocytes exceeds upper threshold of a range of normal values, Heng1: binary, Heng scale, neutrophils: continuous, scaled with min-max (0–1) scaler, amount of neutrophils, MSKCC2: binary, MSKCC (Memorial Sloan Kettering Cancer Center) scale, lymphocytes between 2.13 (inc.) and 5.21 (inc.): binary, 1 if amount of lymphocytes is inside this range, G3: binary, from tumor grading, denotes high grade (tumor poorly differentiated), distant lymph nodes: binary, 1 if tumor metastasis in the distant lymph nodes, number of other cancers: numeric, number of other cancers (metastasis) than: lungs, liver, bones and distant lymph nodes, AH: binary, denotes if someone suffers from arterial hypertension, T2: binary, cancer staging (T1–T4), LDH > 1.5 x UT: binary, lactate dehydrogenase activity, 1 if exceeds 1.5 x upper threshold of a range of normal values, leukocytes between 3.21 (inc.) and 4.49 (inc.): binary, 1 if amount of leukocytes is inside this range, HGB < LT: binary, 1 if hemoglobin concentration exceeds lower threshold of a range of normal values, Ca > UT: binary, 1 if calcium concentration exceeds upper threshold of a range of normal values.

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Pajor, A., Żołnierek, J., Sniezynski, B., Sitek, A. (2022). Effect of Feature Discretization on Classification Performance of Explainable Scoring-Based Machine Learning Model. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_9

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  • DOI: https://doi.org/10.1007/978-3-031-08757-8_9

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