Machine Learning Interpretability in Biostatistics: Making Models Transparent and Trustworthy

Safet Linie^*
*Correspondence: Safet Linie, Department of Biostatistics, University of Dhaka, Dhaka, Bangladesh, Email:

Introduction

Machine Learning (ML) interpretability is a critical concern in biostatistics, especially when developing predictive models for healthcare applications. Interpretability refers to the ability to understand and explain how a machine learning model makes predictions. Machine learning models are increasingly being used to assist healthcare professionals in making clinical decisions. It's crucial that these models provide explanations for their predictions, so doctors can understand and trust the recommendations. Interpretability helps ensure transparency in healthcare AI systems. Knowing how a model arrives at a particular prediction is vital for accountability, especially when those predictions impact patient care. In biostatistics, selecting relevant features (variables) is critical for modelling health outcomes accurately. Interpretability can help identify which features are the most influential in the model's predictions, aiding researchers in understanding the underlying biology or factors contributing to a particular health condition [1].

Description

machine learning and its applications, especially in high-stakes domains like healthcare and criminal justice. There are valid concerns about the use of black-box machine learning models, as well as potential issues associated with trying to explain these models after the fact. Black-box machine learning models, such as deep neural networks, can achieve impressive predictive performance but often lack transparency in how they arrive at decisions. This lack of transparency can lead to mistrust from end-users, whether they are clinicians, judges, or the general public. Post hoc explanations, which involve creating methods to explain black-box models, are one approach to address the transparency issue. However, these explanations may not always provide a full understanding of model behaviour, and they can sometimes be misleading. It's often more reliable to build models with inherent interpretability from the outset. Building models with inherent interpretability means choosing algorithms and techniques that are transparent by design. Decision trees, linear models, and rule-based models are examples of inherently interpretable models. These models provide clear insights into how input features contribute to predictions. In some cases, the choice between interpretable and black-box models may involve a trade-off between model complexity and performance. Interpretable models may have limitations in terms of predictive accuracy, especially when dealing with highly complex and nonlinear data. However, these limitations may be acceptable in situations where transparency and interpretability are paramount.

High-stakes domains, such as healthcare and criminal justice, have profound ethical and societal implications. Making decisions based on blackbox models without clear explanations can lead to unfair or biased outcomes, perpetuating systemic issues. In contrast, interpretable models can help identify and address potential biases. It's essential to ensure that practitioners, policymakers, and stakeholders have the necessary education and expertise to make informed decisions about model selection and use. This includes understanding the trade-offs between model complexity and interpretability. One such application is the use of wearable devices to monitor linear and angular head accelerations in football to detect potential hazardous head impacts. These devices are typically mounted inside the football helmet, and they continuously track the frequency and severity of impacts that a player's head experiences during games or practices [2,3].

This data can be crucial for identifying players at risk of head injuries and for implementing appropriate safety measures to minimize such risks. The information gathered by these devices can also contribute to the on-going research on concussions and traumatic brain injuries in sports. Similarly, in baseball and softball, wearable swing tracker devices have been developed to monitor various swing metrics. These devices are often attached to the player's bat or worn on the wrist, and they provide real-time feedback on metrics such as swing power, swing speed, and hitting zone analysis. Coaches and players can use this data to assess and improve their performance, optimize their swing mechanics, and work on specific aspects of their game. The use of machine learning and deep learning techniques is also gaining prominence, enabling systems to adapt and improve over time based on user feedback and evolving patterns. Additionally, researchers are exploring novel sensing technologies and hardware advancements to capture biometric data more accurately and efficiently. As multimodal biometrics continues to evolve, it holds the potential to revolutionize not only access control but also various other applications where reliable identity verification is essential [4,5].

Conclusion

The ideal approach is to prioritize inherent interpretability in machine learning models, especially in domains where decisions have significant consequences. This approach can help build trust, reduce biases, and ensure that AI systems are accountable and beneficial to society. However, achieving interpretability while maintaining strong predictive performance can be a challenging task and may require innovative research and model development. Achieving interpretability in machine learning models in biostatistics is essential for making these models useful and trustworthy in healthcare applications. It involves selecting appropriate models, using interpretable techniques, collaborating with domain experts, and documenting the process thoroughly to ensure that predictions are transparent, accountable, and clinically relevant

Acknowledgement

We thank the anonymous reviewers for their constructive criticisms of the manuscript. The support from ROMA (Research Optimization and recovery in the Manufacturing industry), of the Research Council of Norway is highly appreciated by the authors.

Conflict of Interest

The Author declares there is no conflict of interest associated with this manuscript.

References

1. Rudin, Cynthia. "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead." Nat Mach Intell 1 (2019): 206-215.

Google Scholar, Crossref, Indexed at

2. Karatekin, T., Sancak, S., Celik, G. and Topcuoglu, S., et al. "Interpretable machine learning in healthcare through generalized additive model with pairwise interactions (GA2M): Predicting severe retinopathy of prematurity.” In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) 61-66.

Google Scholar, Indexed at

3. Kamath, Uday and John Liu. "Explainable artificial intelligence: An introduction to interpretable machine learning.” Springer (2021).

Google Scholar, Crossref, Indexed at

4. He, Xinyu, Ruoyu Tang, Jie Lou and Ruiqi Wang. "Identifying key factors in cell fate decisions by machine learning interpretable strategies ." J Biol Phys (2023): 1-20.

Google Scholar, Crossref, Indexed at

5. Kos, Marko and Iztok Kramberger. "A wearable device and system for movement and biometric data acquisition for sports applications." IEEE 5 (2017): 6411-6420.

Google Scholar, Crossref, Indexed at