Diabetes mellitus is a common disease of human body caused by a group of metabolic disorders where the sugar levels over a prolonged period is very high. It affects different organs of the human body which thus harm a large number of the body's system, in particular the blood veins and nerves. Early prediction in such disease can be controlled and save human life. To achieve the goal, this research work mainly explores various risk factors related to this disease using machine learning techniques. Machine learning techniques provide efficient result to extract knowledge by constructing predicting models from diagnostic medical datasets collected from the diabetic patients. Extracting knowledge from such data can be useful to predict diabetic patients. In this work, we employ four popular machine learning algorithms, namely Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN) and C4.5 Decision Tree, on adult population data to predict diabetic mellitus. Our experimental results show that C4.5 decision tree achieved higher accuracy compared to other machine learning techniques.
A novel method for Causal Structure Discovery from EHR data, a demonstration on type-2 diabetes mellitus
Institute for Health Informatics, University of Minnesota（明尼苏达大学健康信息学研究所）；Mayo Clinic（梅奥诊所）
导言：发现疾病的因果机制有助于更好地诊断、预后和治疗选择。临床试验一直是确定因果关系的黄金标准，但它们是资源密集型的，有时不可行或不道德。电子健康记录（EHR）包含了丰富的真实世界的数据，有希望发现疾病的机制，但现有的因果结构发现（CSD）的方法不足以利用它们，由于EHR数据的特殊性。我们提出了一种新的数据转换方法和一种新的CSD算法，以克服这些特点所带来的挑战。材料和方法：我们证明了所提出的方法上的应用，2型糖尿病。我们使用Mayo Clinic的大型EHR数据集来内部评估拟议的转换和CSD方法，并使用来自独立卫生系统Fairview Health Services的另一个大型数据集作为外部验证。我们比较了我们提出的方法的性能快速贪婪等价搜索（FGES），一个国家的最先进的CSD方法的正确性，稳定性和完整性。我们通过外部验证测试了所提出的算法的通用性。结果和结论：所提出的方法通过成功地结合研究设计考虑来改进现有方法，在面对不可靠的EHR时间戳时具有鲁棒性，并且更正确可靠地推断因果效应方向。所提出的数据转换成功地提高了所发现的图形的临床正确性和跨引导样本的边缘方向的一致性。它导致了优越的准确性，稳定性和完整性。
Secure and Privacy-Preserving Automated Machine Learning Operations into End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction
The University of Melbourne（墨尔本大学）；United Arab Emirates University（阿联酋大学）；Mediclinic（Mediclinic医疗集团）；Bhubaneswar Institute of Technology（布巴内斯瓦尔理工学院）；SRM Institute of Science & Technology（SRM科学技术学院）
Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus
Lobachevsky State University（罗巴切夫斯基国立大学）；Università Politecnica delle Marche（马尔凯理工大学）；IRCCS INRCA（意大利国家养老与老年病研究所）；Mriya Life Institute, National Academy of Active Longevity（国家积极长寿研究院 Mriya Life 研究所）
Improving Early Prediction of Type 2 Diabetes Mellitus with ECG-DiaNet: A Multimodal Neural Network Leveraging Electrocardiogram and Clinical Risk Factors
College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学科学与工程学院）
Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomics Features from Multimodal Retinal Images
Universitat Politècnica de Catalunya (UPC)（加泰罗尼亚理工大学）；Institut Clínic d´Oftalmología (ICOF)（眼科临床研究所）；Hospital Clínic de Barcelona（巴塞罗那临床医院）；Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center（智能数据科学与人工智能研究中心）；August Pi i Sunyer Biomedical Research Institute (IDIBAPS)（奥古斯特·皮伊·苏涅尔生物医学研究所）；School of Medicine, Universitat de Barcelona（巴塞罗那大学医学院）
GLYFE: Review and Benchmark of Personalized Glucose Predictive Models in Type-1 Diabetes
CNRS-LIMSI（法国国家科学研究中心-LIMSI）；Université Paris-Saclay（巴黎萨克雷大学）；Université Paris 8（巴黎第八大学）；Samovar；CNRS（法国国家科学研究中心）；Télécom SudParis（南巴黎电信学院）；Institut Polytechnique de Paris（巴黎综合理工学院）
HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System
United Arab Emirates University（阿联酋大学）；The University of Melbourne（墨尔本大学）
Multi-Modal Machine Learning for Population- and Subject-Specific lncRNA-Type 2 Diabetes Association Analysis
Department of Electronics and Communication Engineering, IIITDM Jabalpur（IIITDM Jabalpur电子与通信工程系）；Department of Electronics and Communication Engineering, Central University of Jammu（Jammu中央大学电子与通信工程系）
Explainable Retinal Imaging for Prediction of Multi-Organ Dysfunction in Type 2 Diabetes
Faculty of Computer Science and Artificial Intelligence（计算机科学与人工智能学院）；Frontier Science Computing Center（前沿科学计算中心）；Chinese Academy of Sciences（中国科学院）；Chinese University of Hong Kong（香港中文大学）；Zhuhai People's Hospital（珠海人民医院）；Beijing Institute of Technology（北京理工大学）；Jinan University（暨南大学）；Lampang Inter-Tech College
Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach
organization= Daniel J. Epstein Department of Industrial \& Systems Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= United States；organization= Colorectal Research Center, Iran University of Medical Sciences , city= Tehran , postcode= 14535 , country= Iran；organization= Department of Health Science, California State University , city= Long Beach , state= CA , postcode= 90840 , country= United States；organization= Department of Industrial；Manufacturing Engineering, California State Polytechnic University , city= Pomona , state= CA , postcode= 91768 , country= United States
Detecting diabetic retinopathy severity through fundus images using an ensemble of classifiers
Technical University of Cluj-Napoca（克卢日-纳波卡技术大学）；“Iuliu Hatieganu” University of Medicine and Pharmacy（“尤利乌·哈采加努”医药大学）；Emergency County Hospital（急诊县医院）
A Structural FHMM for Interpretable Disease Trajectories in T2DM
Swiss Data Science Center (SDSC)（瑞士数据科学中心（SDSC））；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；ETH Zürich（苏黎世联邦理工学院）；Institute of Pharmaceutical Sciences（药学院）
ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with Automatic Multilayer Perceptron for Diabetes Prediction
National University of Sciences & Technology（国家科学与技术大学）；Manchester Metropolitan University（曼彻斯特城市大学）
随着先进的数据分析技术，为疾病预测提供更准确的决策支持系统的努力正在增加。世界卫生组织（WHO）的调查表明，糖尿病患者和相关死亡人数每年都在大幅增加。糖尿病的早期诊断是研究人员和从业人员的主要关注点。本文提出了一个应用\textit{Automatic Multilayer Perceptron }，\textit{ }结合离群检测方法\textit{Enhanced Class Outlier Detection using distance based algorithm }，创建一个预测框架命名为增强类离群自动多层感知器（ECO-AMLP）。一系列的实验进行公开的皮马印度糖尿病数据集比较ECO-AMLP与其他个人分类器以及集成为基础的方法。与其他预处理和分类技术相比，我们框架中使用的离群值技术得到了更好的结果。最后，将结果与文献中报道的其他最先进的方法进行比较，预测PIDD糖尿病的准确性为88.7%，优于所有其他报道的研究。
A novel solution of deep learning for enhanced support vector machine for predicting the onset of type 2 diabetes
Charles Sturt University（查尔斯特大学）；Al Iraqia University（伊拉克大学）；Western Sydney University（西悉尼大学）；Kent Institute Australia（澳大利亚肯特学院）；Asia Pacific International College（亚太国际学院）；University of Kurdistan Hewler（库尔德斯坦休勒大学）；University of New South Wales（新南威尔士大学）
Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction
Insight Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland（数据分析洞察中心、计算学院、都柏林城市大学，都柏林，爱尔兰）；Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam（信息技术学院、胡志明市开放大学，胡志明市，越南）；Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City, Vietnam（电气与电子工程学院、胡志明市交通大学，胡志明市，越南）；School of Public Health, University College Cork, Cork, Ireland（公共卫生学院、科克大学学院，科克，爱尔兰）
CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data
School of Computing, Wichita State University（威奇托州立大学计算学院）；Department of Computer Science and Engineering, American International University-Bangladesh（孟加拉国美国国际大学计算机科学与工程系）；School of Computer Science and Engineering, The University of Aizu（会津大学计算机科学与工程学院）
