利用社会人口统计学特征预测糖尿病的机器学习方法

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Algorithms Pub Date : 2023-10-29 DOI:10.3390/a16110503
Md. Ashikur Rahman, Lway Faisal Abdulrazak, Md. Mamun Ali, Imran Mahmud, Kawsar Ahmed, Francis M. Bui
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引用次数: 0

摘要

糖尿病是一种致命疾病,对人体其他疾病的发展起着至关重要的作用。从临床角度来看,减轻糖尿病影响的最重要方法是早期控制和管理,以潜在的治愈为目标。然而,缺乏认识和昂贵的临床检测是孟加拉国、巴基斯坦和印度等低收入国家忽视临床诊断和预防措施的主要原因。从这个角度来看,本研究旨在建立一个自动机器学习(ML)模型,该模型将使用社会人口统计学特征而不是临床属性来预测早期阶段的糖尿病,因为临床特征并不总是适用于低收入国家的所有人。为了找到模型的有监督ML分类器的最佳拟合,我们应用了六种分类算法,发现RF以99.36%的准确率优于模型。此外,所有应用的分类器根据SHAP值发现了最显著的危险因素。本研究表明,多尿、多饮和延迟愈合是发展为糖尿病的最重要的危险因素。研究结果表明,该模型对早期糖尿病的预测能力很强。
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Machine Learning-Based Approach for Predicting Diabetes Employing Socio-Demographic Characteristics
Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. From a clinical perspective, the most significant approach to mitigating the effects of diabetes is early-stage control and management, with the aim of a potential cure. However, lack of awareness and expensive clinical tests are the primary reasons why clinical diagnosis and preventive measures are neglected in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, this study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes, due to the fact that clinical features are not always accessible to all people from lower-income countries. To find the best fit of the supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. This study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for developing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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