## 4 Discussion  
Although explanations of CDSS outputs are deemed highly important for the appropriate use and acceptance of these systems, it is not yet clear what type of explanations can be more useful for their end-users. In the era of XAI, a variety of techniques to explain machine learning models are available, but little is known about their correlation with user-understanding [13]. We conducted a user study with healthcare practitioners of pregnant women in Ireland to investigate and compare two different types of XAI methods based on the machine learning-based CDSS we developed to predict GDM among women with overweight and obesity. Our results, primarily based on obstetricians, showed that a CDSS, explained by either feature contribution or example, can have a substantial impact on the decision-making of  
Figure 9: WOA for feature contribution and example for participants who preferred explanation by feature contribution. healthcare practitioners, demonstrating the effectiveness of our explainable CDSS. It has been argued in literature, that example-based methods such as k-nearest neighbors (KNN), are motivated by human-reasoning [28; 29; 30; 31]; thus, they may be appropriate for machine learning models used in the medical domain, as they can explain the model's reasoning by allowing the user to compare it to similar case(s) and also apply their expertise to assess the quality of the suggestion [32]. In our work, we found no difference between the two XAI methods, explanation by feature contribution and explanation by example, regarding the advice-taking from a CDSS. Additionally, we found that both methods may lead to over-reliance issues, i.e., healthcare practitioners may be misled by an incorrect prediction of the CDSS. A previous study that compared rule-based and example-based explanations found that both types of explanations also led to over-reliance on the model's predictions, although the participants in that study were not healthcare practitioners [33]. Despite many participating healthcare practitioners preferring both types of explanation, we noticed a clear preference for explanation by feature contribution over explanation by example, while the preference for XAI methods differed according to the expertise of healthcare practitioners. More than half of the obstetricians preferred an explanation by feature contribution only, whereas all participating dietitians preferred to have both an explanation by feature contribution and by example. The vast majority of participants preferred to have at least one of the XAI methods in a CDSS; therefore, we suggest that CDSS developers should incorporate some XAI methods in AI-based CDSSs where possible. It might be helpful to include multiple options of explanations for healthcare practitioners with different clinical expertise and cognitive styles. Healthcare practitioners have personal preferences concerning the information that they provide in their diagnosis [17], which would affect their preferred type of displayed information and explanation by a CDSS. In addition, although most participating healthcare practitioners had no prior experience of using a CDSS, the majority were highly inclined to use a CDSS, indicating the potential for CDSS use in clinical settings. We identified the inclination towards CDSS use as an important factor that impacts the advice-taking among obstetricians. Computer-based training [34], involving machine learning education and its application in medical schools [35], may improve healthcare practitioners' inclination towards technology use and may eventually enhance the adoption of machine learning-based systems in clinical practice. We did not find any association between the advice-taking of healthcare practitioners and their clinical expertise, years of experience or their prior experience of CDSS use. One of the limitations of this study is the small sample size despite our best efforts for study participation.