Background Allergy is a hypersensitive reaction that occurs when the allergen reacts with the immune system. The prevalence and severity of the allergies are uprising in South Asian countries. Allergy often occurs in combinations which becomes difficult for physicians to diagnose.

Objectives This work aims to develop a decision-making model which aids physicians in diagnosing allergy comorbidities. The model intends to not only provide rational decisions, but also explainable knowledge about all alternatives.

Methods The allergy data gathered from real-time sources contain a smaller number of samples for comorbidities. Decision-making model applies three sampling strategies, namely, ideal, single, and complete, to balance the data. Bayes theorem-based probabilistic approaches are used to extract knowledge from the balanced data. Preference weights for attributes with respect to alternatives are gathered from a group of domain-experts affiliated to different allergy testing centers. The weights are combined with objective knowledge to assign confidence values to alternatives. The system provides these values along with explanations to aid decision-makers in choosing an optimal decision.

Results Metrics of explainability and user satisfaction are used to evaluate the effectiveness of the system in real-time diagnosis. Fleiss' Kappa statistic is 0.48, and hence the diagnosis of experts is said to be in moderate agreement. The decision-making model provides a maximum of 10 suitable and relevant pieces of evidence to explain a decision alternative. Clinicians have improved their diagnostic performance by 3% after using CDSS (77.93%) with a decrease in 20% of time taken.

No human/animal subjects are directly involved in this study.

The implementation of interpretable medical decision-making model which can provide support for diagnosing allergy comorbidities would aid clinicians with explainable knowledge about decision space. We developed the model based on the data gathered from real-time sources and the preferences provided by different clinicians. This helps in understanding the agreement among the clinicians in allergy diagnosis for customizing the model's knowledge accordingly.

Received: 04 March 2022

Accepted: 06 July 2022

Article published online:
11 October 2022

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