This paper aims to conduct a systematic literature review(SLR)using an artificial intelligence(AI)approach to predict and diagnose diabetes mellitus.After reviewing the literature published from 2015–2025,the paper a...This paper aims to conduct a systematic literature review(SLR)using an artificial intelligence(AI)approach to predict and diagnose diabetes mellitus.After reviewing the literature published from 2015–2025,the paper aims to identify the most effective AI techniques,the most used datasets,the most widely used data preprocessing techniques,and the most common issues.After analyzing the literature,it has been found that convolutional neural networks(CNNs)and long short-term memory(LSTM)networks are deep learning models that have shown high accuracy in diabetes prediction.Recursive feature elimination(RFE)and SMOTE are feature selection techniques that have significantly improved model accuracy,training time,and interpretability.Amidst this technological advancement,some existing issues persist:data imbalance,the inapplicability of techniques,computational limitations,and a lack of real-time application in a healthcare environment.The literature review has also identified the need for robust,interpretable,and scalable AI systems capable of handling large volumes of data,including real-world data,in the healthcare industry.Furthermore,it has been identified that the benefits should be integrated with wearable health monitoring systems and the development of privacy-preserving models to ensure continuous,secure,and proactive diabetes management.展开更多
文摘This paper aims to conduct a systematic literature review(SLR)using an artificial intelligence(AI)approach to predict and diagnose diabetes mellitus.After reviewing the literature published from 2015–2025,the paper aims to identify the most effective AI techniques,the most used datasets,the most widely used data preprocessing techniques,and the most common issues.After analyzing the literature,it has been found that convolutional neural networks(CNNs)and long short-term memory(LSTM)networks are deep learning models that have shown high accuracy in diabetes prediction.Recursive feature elimination(RFE)and SMOTE are feature selection techniques that have significantly improved model accuracy,training time,and interpretability.Amidst this technological advancement,some existing issues persist:data imbalance,the inapplicability of techniques,computational limitations,and a lack of real-time application in a healthcare environment.The literature review has also identified the need for robust,interpretable,and scalable AI systems capable of handling large volumes of data,including real-world data,in the healthcare industry.Furthermore,it has been identified that the benefits should be integrated with wearable health monitoring systems and the development of privacy-preserving models to ensure continuous,secure,and proactive diabetes management.
