Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets

• URL: http://arxiv.org/abs/2206.02056v1
• Date: Sat, 4 Jun 2022 20:20:39 GMT
• Title: Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets
• Authors: Sreejita Ghosh (1, 5,6), Elizabeth S. Baranowski (2), Michael Biehl (1,2,3), Wiebke Arlt (2), Peter Tino (4), and Kerstin Bunte (1) ((1) Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University of Groningen, The Netherlands (2) Institute of Metabolism and Systems Research, University of Birmingham, the United Kingdom (3) Systems Modelling and Quantitative Biomedicine, IMSR, University of Birmingham, the United Kingdom (4) School of Computer Science, University of Birmingham, the United Kingdom (5) Utrecht University, The Netherlands (6) University Medical Centrum Utrecht, The Netherlands)
• Abstract summary: Application of interpretable machine learning techniques on medical datasets facilitate early and fast diagnoses, along with getting deeper insight into the data. Medical datasets face common issues such as heterogeneous measurements, imbalanced classes with limited sample size, and missing data. We present a family of prototype-based (PB) interpretable models which are capable of handling these issues.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Application of interpretable machine learning techniques on medical datasets facilitate early and fast diagnoses, along with getting deeper insight into the data. Furthermore, the transparency of these models increase trust among application domain experts. Medical datasets face common issues such as heterogeneous measurements, imbalanced classes with limited sample size, and missing data, which hinder the straightforward application of machine learning techniques. In this paper we present a family of prototype-based (PB) interpretable models which are capable of handling these issues. The models introduced in this contribution show comparable or superior performance to alternative techniques applicable in such situations. However, unlike ensemble based models, which have to compromise on easy interpretation, the PB models here do not. Moreover we propose a strategy of harnessing the power of ensembles while maintaining the intrinsic interpretability of the PB models, by averaging the model parameter manifolds. All the models were evaluated on a synthetic (publicly available dataset) in addition to detailed analyses of two real-world medical datasets (one publicly available). Results indicated that the models and strategies we introduced addressed the challenges of real-world medical data, while remaining computationally inexpensive and transparent, as well as similar or superior in performance compared to their alternatives.

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