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An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI

Sensors (Basel, Switzerland), 2022-09, Vol.22 (19), p.7268 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22197268 ;PMID: 36236367

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  • Title:
    An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI
  • Author: Kibria, Hafsa Binte ; Nahiduzzaman, Md ; Goni, Md. Omaer Faruq ; Ahsan, Mominul ; Haider, Julfikar
  • Subjects: Accuracy ; Algorithms ; Analysis ; Artificial intelligence ; artificial intelligence (AI) ; Chronic diseases ; Classifiers ; Data mining ; Datasets ; Developing countries ; Development and progression ; Diabetes ; Diabetes mellitus ; Disease prevention ; ensemble classifier ; explainable AI ; Graphical representations ; Insulin resistance ; Laboratories ; LDCs ; Machine learning ; machine learning (ML) ; Medical care ; Neural networks ; Physicians ; Prediction models ; Quality management ; soft voting ; Support vector machines ; Type 2 diabetes
  • Is Part Of: Sensors (Basel, Switzerland), 2022-09, Vol.22 (19), p.7268
  • Description: Diabetes is a chronic disease that continues to be a primary and worldwide health concern since the health of the entire population has been affected by it. Over the years, many academics have attempted to develop a reliable diabetes prediction model using machine learning (ML) algorithms. However, these research investigations have had a minimal impact on clinical practice as the current studies focus mainly on improving the performance of complicated ML models while ignoring their explainability to clinical situations. Therefore, the physicians find it difficult to understand these models and rarely trust them for clinical use. In this study, a carefully constructed, efficient, and interpretable diabetes detection method using an explainable AI has been proposed. The Pima Indian diabetes dataset was used, containing a total of 768 instances where 268 are diabetic, and 500 cases are non-diabetic with several diabetic attributes. Here, six machine learning algorithms (artificial neural network (ANN), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost, XGBoost) have been used along with an ensemble classifier to diagnose the diabetes disease. For each machine learning model, global and local explanations have been produced using the Shapley additive explanations (SHAP), which are represented in different types of graphs to help physicians in understanding the model predictions. The balanced accuracy of the developed weighted ensemble model was 90% with a F1 score of 89% using a five-fold cross-validation (CV). The median values were used for the imputation of the missing values and the synthetic minority oversampling technique (SMOTETomek) was used to balance the classes of the dataset. The proposed approach can improve the clinical understanding of a diabetes diagnosis and help in taking necessary action at the very early stages of the disease.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s22197268
    PMID: 36236367
  • Source: GFMER Free Medical Journals
    PubMed Central
    Coronavirus Research Database
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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