Analyzing classification and feature selection strategies for diabetes prediction across diverse diabetes datasets.

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2024-08-21 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1421751
Jayakumar Kaliappan, I J Saravana Kumar, S Sundaravelan, T Anesh, R R Rithik, Yashbir Singh, Diana V Vera-Garcia, Yassine Himeur, Wathiq Mansoor, Shadi Atalla, Kathiravan Srinivasan
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Abstract

Introduction: In the evolving landscape of healthcare and medicine, the merging of extensive medical datasets with the powerful capabilities of machine learning (ML) models presents a significant opportunity for transforming diagnostics, treatments, and patient care.

Methods: This research paper delves into the realm of data-driven healthcare, placing a special focus on identifying the most effective ML models for diabetes prediction and uncovering the critical features that aid in this prediction. The prediction performance is analyzed using a variety of ML models, such as Random Forest (RF), XG Boost (XGB), Linear Regression (LR), Gradient Boosting (GB), and Support VectorMachine (SVM), across numerousmedical datasets. The study of feature importance is conducted using methods including Filter-based, Wrapper-based techniques, and Explainable Artificial Intelligence (Explainable AI). By utilizing Explainable AI techniques, specifically Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), the decision-making process of the models is ensured to be transparent, thereby bolstering trust in AI-driven decisions.

Results: Features identified by RF in Wrapper-based techniques and the Chi-square in Filter-based techniques have been shown to enhance prediction performance. A notable precision and recall values, reaching up to 0.9 is achieved in predicting diabetes.

Discussion: Both approaches are found to assign considerable importance to features like age, family history of diabetes, polyuria, polydipsia, and high blood pressure, which are strongly associated with diabetes. In this age of data-driven healthcare, the research presented here aspires to substantially improve healthcare outcomes.

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分析不同糖尿病数据集的糖尿病预测分类和特征选择策略。
简介在不断发展的医疗保健领域,广泛的医疗数据集与机器学习(ML)模型的强大功能相结合,为诊断、治疗和患者护理的变革带来了重大机遇:本研究论文深入探讨了数据驱动的医疗保健领域,重点是确定最有效的糖尿病预测 ML 模型,并揭示有助于该预测的关键特征。在众多医疗数据集中,使用随机森林(RF)、XG Boost(XGB)、线性回归(LR)、梯度提升(GB)和支持向量机(SVM)等多种 ML 模型分析了预测性能。研究特征重要性的方法包括基于过滤器的技术、基于封装的技术和可解释人工智能(Explainable AI)。通过利用可解释的人工智能技术,特别是本地可解释模型-不可知解释(LIME)和SHAPLE Additive exPlanations(SHAP),确保了模型决策过程的透明性,从而增强了对人工智能驱动决策的信任:结果:在基于封装的技术中,通过射频识别的特征和在基于过滤器的技术中,通过奇偶校验识别的特征都能提高预测性能。在预测糖尿病方面,精确度和召回值显著提高,达到 0.9:这两种方法都非常重视年龄、糖尿病家族史、多尿、多饮和高血压等与糖尿病密切相关的特征。在这个数据驱动医疗保健的时代,本文介绍的研究有望大幅改善医疗保健结果。
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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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