Interpretable machine learning for dermatological disease detection: Bridging the gap between accuracy and explainability

Yusra Nasir, Karuna Kadian, Arun Sharma, Vimal Dwivedi

Research output: Contribution to journalArticlepeer-review

Abstract

Research on disease detection by leveraging machine learning techniques has been under significant focus. The use of machine learning techniques is important to detect critical diseases promptly and provide the appropriate treatment. Disease detection is a vital and sensitive task and while machine learning models may provide a robust solution, they can come across as complex and unintuitive. Therefore, it is important to gauge a better understanding of the predictions and trust the results. This paper takes up the crucial task of skin disease detection and introduces a hybrid machine learning model combining SVM and XGBoost for the detection task. The proposed model outperformed the existing machine learning models — Support Vector Machine (SVM), decision tree, and XGBoost with an accuracy of 99.26%. The increased accuracy is essential for detecting skin disease due to the similarity in the symptoms which make it challenging to differentiate between the different conditions. In order to foster trust and gain insights into the results we turn to the promising field of Explainable Artificial Intelligence (XAI). We explore two such frameworks for local as well as global explanations for these machine learning models namely, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME).
Original languageEnglish
Article number108919
Pages (from-to)1-14
Number of pages14
JournalComputers in biology and medicine
Volume179
Early online date23 Jul 2024
DOIs
Publication statusPublished (in print/issue) - 30 Sept 2024

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Ltd

Keywords

  • SVM
  • Decision tree
  • XGBoost
  • eXplainable AI
  • SHAP
  • LIME

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