Atrial fibrillation (AF) is a common cardiac rhythm disorder, which increases the risk of ischemic stroke and other thromboembolism (TE). Accurate prediction of TE is highly valuable for early intervention to AF patients. However, the prediction performance of previous TE risk models for AF is not satisfactory. In this study, we used integrated machine learning and data mining approaches to bui...

BACKGROUND Framingham Stroke Risk Score (FSRS) is the most well-regarded risk appraisal tools for evaluating an individual's absolute risk on stroke onset. However, several widely accepted risk factors for stroke were not included in the original Framingham model. This study proposed a new model which combines an existing risk models with new risk factors using synthesis analysis, and applied i...

Genetic risk prediction is an important goal in human genetics research and precision medicine. Accurate prediction models will have great impacts on both disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome wide association studies (GWAS), genetic risk prediction accuracy remains moderate for most disea...

Risk prediction models are critical in managing patients with acute coronary syndromes (ACS) as they identify high-risk patients who benefit the most from targeted care. We discuss the process of developing and validating a risk prediction model as well as highlight the more commonly used models in clinical practice currently. Finally we conclude by outlining the importance of creating a risk p...

Purpose of Review The central question of preoperative assessment is not "What can be done?" but "What should be done and how?" Predicting a patient's risk of unwanted outcomes is vital to answering this question. This review discusses risk prediction tools currently available and anticipates future developments. Recent Findings Simple, parsimonious risk scales and scores are being replaced b...

 In this paper, a new Dynamic Geometric Genetic Programming (DGGP) technique is applied to empirical analysis of financial ratios and bankruptcy prediction. Financial ratios are indeed desirable for prediction of corporate bankruptcy and identification of firms’ impending failure for investors, creditors, borrowing firms, and governments. By the time, several methods have been attempted in...

the prediction of lithology is necessary in all areas of petroleum engineering. this means that todesign a project in any branch of petroleum engineering, the lithology must be well known. supportvector machines (svm’s) use an analytical approach to classification based on statistical learningtheory, the principles of structural risk minimization, and empirical risk minimization. in thisresearc...

Coronary heart disease (CHD) is a significant cause of morbidity and mortality worldwide. Many risk prediction models have been developed in an effort to assist clinicians in risk assessment and the prevention of CHD. However, it is unclear whether the existing CHD prediction tools can improve clinical performance, and recently, there has been a lot of effort being made to improve the accuracy ...

The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this res...