• machine learning
• premenopausal women
• vitamin D

The prevalence of type 2 diabetes (T2D) has been increasing dramatically in recent decades, and 47.5% of T2D patients will die of cardiovascular disease. Thallium-201 myocardial perfusion scan (MPS) is a precise and non-invasive method to detect coronary artery disease (CAD). Most previous studies used traditional logistic regression (LGR) to evaluate the risks for abnormal CAD. Rapidly developing machine learning (Mach-L) techniques could potentially outperform LGR in capturing non-linear relationships.

To aims were: (1) Compare the accuracy of Mach-L methods and LGR; and (2) Found the most important factors for abnormal TMPS.

556 T2D were enrolled in the study (287 men and 269 women). Demographic and biochemistry data were used as independent variables and the sum of stressed score derived from MPS scan was the dependent variable. Subjects with a MPS score ≥ 9 were defined as abnormal. In addition to traditional LGR, classification and regression tree (CART), random forest, Naïve Bayes, and eXtreme gradient boosting were also applied. Sensitivity, specificity, accuracy and area under the receiver operation curve were used to evaluate the respective accuracy of LGR and Mach-L methods.

Except for CART, the other Mach-L methods outperformed LGR, with gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking emerging as the most important factors to predict abnormal MPS.

Four Mach-L methods are found to outperform LGR in predicting abnormal TMPS in Chinese T2D, with the most important risk factors being gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking.

• Myocardial perfusion scintigraphy
• Machine learning
• Type 2 diabetes
• Thallium-201

• Female
• Humans
• Bayes Theorem
• East Asian People/statistics & numerical data
• Linear Models
• Machine Learning
• Osteoporosis/epidemiology
• Risk Factors
• Middle Aged
• Risk Assessment/methods
• Taiwan/epidemiology

• Biochemistry data
• Lifestyle
• Machine learning
• Renal function

• environmental impact
• risk factors
• statistics

• health screening indicator
• machine learning
• male reproductive health
• sleep time
• sperm quality

• NSC-RD-110-1-10-503 Far Eastern Memorial Hospital
• NSTC 111-2221-E-030-009-; NSTC-110-2221-E-161-003 National Science and Technology Council, Taiwan

Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a non-invasive and extensively used tool in recognizing CAD in clinical settings. In this study, we attempted to compare the predictive accuracy of evaluating abnormal Th-201 scans using traditional multiple linear regression (MLR) with four machine learning (ML) methods. From the study, we can determine whether ML surpasses traditional MLR and rank the clinical variables and compare them with previous reports.In total, 796 T2DM, including 368 men and 528 women, were enrolled. In addition to traditional MLR, classification and regression tree (CART), random forest (RF), stochastic gradient boosting (SGB) and eXtreme gradient boosting (XGBoost) were also used to analyze abnormal Th-201 scans. Stress sum score was used as the endpoint (dependent variable). Our findings show that all four root mean square errors of ML are smaller than with MLR, which implies that ML is more precise than MLR in determining abnormal Th-201 scans by using clinical parameters. The first seven factors, from the most important to the least are:body mass index, hemoglobin, age, glycated hemoglobin, Creatinine, systolic and diastolic blood pressure. In conclusion, ML is not inferior to traditional MLR in predicting abnormal Th-201 scans, and the most important factors are body mass index, hemoglobin, age, glycated hemoglobin, creatinine, systolic and diastolic blood pressure. ML methods are superior in these kinds of studies.

• machine learning
• nephropathy
• type 2 diabetes
• urine albumin-creatinine ratio

Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.

• big data
• decision models
• public health

• Big Data
• Clinical Decision-Making
• Data Mining
• Machine Learning
• Public Health