AIMC Topic: Decision Trees

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Utilizing Artificial Intelligence for Predicting Postoperative Complications in Breast Reduction Surgery: A Comprehensive Retrospective Analysis of Predictive Features and Outcomes.

Aesthetic surgery journal
BACKGROUND: Breast reduction is a common procedure with growing rates in the United States of America, aimed at alleviating the physical and psychological burdens of macromastia. Despite high success rates, it carries a risk of complications, with in...

Investigating AI Approaches for Survival Prediction in Chronic Lymphocytic Leukemia.

Studies in health technology and informatics
Chronic lymphocytic leukemia (CLL) exhibits a heterogeneous clinical course. Prognostic markers that impact patient outcomes have been identified, including MYC gene abnormalities. This study investigates machine learning (ML) models for predicting s...

AI-driven health analysis for emerging respiratory diseases: A case study of Yemen patients using COVID-19 data.

Mathematical biosciences and engineering : MBE
In low-income and resource-limited countries, distinguishing COVID-19 from other respiratory diseases is challenging due to similar symptoms and the prevalence of comorbidities. In Yemen, acute comorbidities further complicate the differentiation bet...

Machine learning model using immune indicators to predict outcomes in early liver cancer.

World journal of gastroenterology
BACKGROUND: Patients with early-stage hepatocellular carcinoma (HCC) generally have good survival rates following surgical resection. However, a subset of these patients experience recurrence within five years post-surgery.

Comparison of Predictive Models for Keloid Recurrence Based on Machine Learning.

Journal of cosmetic dermatology
OBJECTIVES: To establish, evaluate and compare three recurrence prediction models for keloid patients using machine learning methods.

Development and Validation of a Novel Model to Discriminate Idiosyncratic Drug-Induced Liver Injury and Autoimmune Hepatitis.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND AND AIM: Discriminating between idiosyncratic drug-induced liver injury (DILI) and autoimmune hepatitis (AIH) is critical yet challenging. We aim to develop and validate a machine learning (ML)-based model to aid in this differentiation.

Detection of pediatric developmental delay with machine learning technologies.

PloS one
OBJECTIVE: Accurate identification of children who will develop delay (DD) is challenging for therapists because recent studies have reported that children who underwent early intervention achieved more favorable outcomes than those who did not. In t...

Developing a smart system for binary classification of disordered voices using machine learning.

American journal of otolaryngology
OBJECTIVES: Voice disorder is characterized by disruptions in voice quality caused by issues in vocal fold vibration during phonation. The study explored the application of machine learning, based on the Random Forest (RF) and Decision Tree (DT) mode...

A machine learning framework to adjust for learning effects in medical device safety evaluation.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning cu...