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Logistic Models

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Outbound Data Legality Analysis in CPTPP Countries under the Environment of Cross-Border Data Flow Governance.

Journal of environmental and public health
The governance of cross-border data flows around the digital economy, data security, and data sovereignty has become a crucial global governance issue. This paper evaluates the legitimacy of data exit rules of CPTPP countries based on machine learnin...

GAIN: A Gated Adaptive Feature Interaction Network for Click-Through Rate Prediction.

Sensors (Basel, Switzerland)
CTR (Click-Through Rate) prediction has attracted more and more attention from academia and industry for its significant contribution to revenue. In the last decade, learning feature interactions have become a mainstream research direction, and dozen...

Predictive Models for Knee Pain in Middle-Aged and Elderly Individuals Based on Machine Learning Methods.

Computational and mathematical methods in medicine
AIM: This study used machine learning methods to develop a prediction model for knee pain in middle-aged and elderly individuals.

Machine learning for cell type classification from single nucleus RNA sequencing data.

PloS one
With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into sp...

The Evaluation on the Credit Risk of Enterprises with the CNN-LSTM-ATT Model.

Computational intelligence and neuroscience
Credit evaluation is a difficult problem in the process of financing and loan for small and medium-sized enterprises. Due to the high dimension and nonlinearity of enterprise behavior data, traditional logistic regression (LR), random forest (RF), an...

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 ...