AIMC Topic: Logistic Models

Clear Filters Showing 431 to 440 of 1261 articles

Prediction and Analysis of Financial Default Loan Behavior Based on Machine Learning Model.

Computational intelligence and neuroscience
In recent years, the increase of customer loan risk and the aggravation of the epidemic have led to the increase of customer default risk. Therefore, identifying high-risk customers has become an important research hotspot for banks. The customer's c...

Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study.

BMC anesthesiology
BACKGROUND: Postoperative major adverse cardiovascular events (MACEs) account for more than one-third of perioperative deaths. Geriatric patients are more vulnerable to postoperative MACEs than younger patients. Identifying high-risk patients in adva...

Machine learning in project analytics: a data-driven framework and case study.

Scientific reports
The analytic procedures incorporated to facilitate the delivery of projects are often referred to as project analytics. Existing techniques focus on retrospective reporting and understanding the underlying relationships to make informed decisions. Al...

Weakly Semi-supervised phenotyping using Electronic Health records.

Journal of biomedical informatics
OBJECTIVE: Electronic Health Record (EHR) based phenotyping is a crucial yet challenging problem in the biomedical field. Though clinicians typically determine patient-level diagnoses via manual chart review, the sheer volume and heterogeneity of EHR...

Machine learning models to prognose 30-Day Mortality in Postoperative Disseminated Cancer Patients.

Surgical oncology
Patients with disseminated cancer at higher risk for postoperative mortality see improved outcomes with altered clinical management. Being able to risk stratify patients immediately after their index surgery to flag high risk patients for healthcare ...

Machine learning to predict adverse outcomes after cardiac surgery: A systematic review and meta-analysis.

Journal of cardiac surgery
BACKGROUND: Machine learning (ML) models are promising tools for predicting adverse postoperative outcomes in cardiac surgery, yet have not translated to routine clinical use. We conducted a systematic review and meta-analysis to assess the predictiv...

A Probability-Based Models Ranking Approach: An Alternative Method of Machine-Learning Model Performance Assessment.

Sensors (Basel, Switzerland)
Performance measures are crucial in selecting the best machine learning model for a given problem. Estimating classical model performance measures by subsampling methods like bagging or cross-validation has several weaknesses. The most important ones...

Separation of Different Blogs from Skin Disease Data using Artificial Intelligence.

Computational intelligence and neuroscience
A combination of environmental conditions may cause skin illness everywhere on the earth, and it is one of the most dangerous diseases that can develop as a result. A major goal in the selection of characteristics is to produce predictions about skin...

Prediction of mortality risk of health checkup participants using machine learning-based models: the J-SHC study.

Scientific reports
Early detection and treatment of diseases through health checkups are effective in improving life expectancy. In this study, we compared the predictive ability for 5-year mortality between two machine learning-based models (gradient boosting decision...

Artificial Intelligence in Allergy and Immunology: Comparing Risk Prediction Models to Help Screen Inborn Errors of Immunity.

International archives of allergy and immunology
BACKGROUND: Inborn errors of immunity (IEI) are underdiagnosed disorders, leading to increased morbimortality and expenses for healthcare system.