Acute ischemic stroke prediction and predictive factors analysis using hematological indicators in elderly hypertensives post-transient ischemic attack

C Shu, C Zheng, D Luo, J Song, Z Jiang, L Ge - Scientific Reports, 2024 - nature.com
Elderly hypertensive patients diagnosed with transient ischemic attack (TIA) are at a
heightened risk for developing acute ischemic stroke (AIS). This underscores the critical …

Does machine learning have a high performance to predict obesity among adults and older adults? A systematic review and meta-analysis

FM Delpino, ÂK Costa, MC do Nascimento… - Nutrition, Metabolism …, 2024 - Elsevier
Abstract Background & Aims Machine learning may be a tool with the potential for obesity
prediction. This study aims to review the literature on the performance of machine learning …

Machine learning allows robust classification of visceral fat in women with obesity using common laboratory metrics

F Palmieri, NF Akhtar, A Pané, A Jiménez… - Scientific Reports, 2024 - nature.com
The excessive accumulation and malfunctioning of visceral adipose tissue (VAT) is a major
determinant of increased risk of obesity-related comorbidities. Thus, risk stratification of …

[HTML][HTML] An investigation of ensemble learning techniques for obesity risk prediction using lifestyle data

SM Ganie, BB Reddy, K Hemachandran… - Decision Analytics …, 2024 - Elsevier
Obesity disease is a significant health issue and has affected millions of people worldwide.
Identifying underlying reasons for the onset of obesity risk in its early stage has become …

Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery

FH Yagin, F Al-Hashem, I Ahmad, F Ahmad… - Nutrients, 2024 - mdpi.com
Background: This study aims to identify unique metabolomics biomarkers associated with
Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based …

[HTML][HTML] Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach

FH Yagin, U Aygun, A Algarni, C Colak… - Journal of Clinical …, 2024 - mdpi.com
Background: Sepsis is characterized by an atypical immune response to infection and is a
dangerous health problem leading to significant mortality. Current diagnostic methods …

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model

U Aygun, FH Yagin, B Yagin, S Yasar, C Colak… - Diagnostics, 2024 - mdpi.com
This study aims to develop an interpretable prediction model based on explainable artificial
intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 …

Predictive Performance of Machine Learning Algorithms Regarding Obesity Levels Based on Physical Activity and Nutritional Habits: A Comprehensive Analysis

PHP de Lucena, LML de Campos… - IEEE Latin America …, 2024 - ieeexplore.ieee.org
Obesity is a complex chronic disease resulting from the interaction of multiple behavioral
factors. This paper presentsthe application of Machine Learning to identify the primary …

Cluster Analysis Of Obesity Risk Levels Using K-Means And Dbscan Methods.

D Geovani, Z Umari… - Computer Engineering & …, 2024 - search.ebscohost.com
Obesity is defined as excessive fat accumulation and abnormal accumulation of adipose
tissue in the human body that poses health risks. The causes of obesity are multifactorial …

The Role of Machine Learning in Obesity Prediction Across Latin American Populations: A Study on the Effectiveness of Different Approaches

S Pamu, JP Vemuri - International Conference on Data Science and …, 2024 - Springer
Obesity has become a dominant health concern globally and it is surpassing undernutrition
and contagious diseases as a major contributor to ill health issues. This alarming trend …