AIMC Topic: Machine Learning

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A machine learning approach to predicting postoperative recurrence in pediatric chronic rhinosinusitis: identification of key metabolic biomarkers.

American journal of otolaryngology
BACKGROUND: Pediatric chronic rhinosinusitis (CRS) is a common chronic inflammatory disease with a high recurrence rate after surgery. This study aimed to construct and validate a machine learning-based predictive model to predict the risk of postope...

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...

Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy.

PloS one
Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperative infection allows a timely administration of appropriate antibiotic treatment. However, doctors ca...

Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis.

PloS one
BACKGROUND: Post-COVID conditions (PCC) have proven difficult to diagnose. In this retrospective observational study, we aimed to characterize the level of variation in PCC diagnoses observed across clinicians from a number of methodological angles a...

An efficient leukemia prediction method using machine learning and deep learning with selected features.

PloS one
Leukemia is a serious problem affecting both children and adults, leading to death if left untreated. Leukemia is a kind of blood cancer described by the rapid proliferation of abnormal blood cells. An early, trustworthy, and precise identification o...

Adaptive mechanism-based grey wolf optimizer for feature selection in high-dimensional classification.

PloS one
Feature Selection (FS) is a crucial component of machine learning and data mining. Its goal is to eliminate redundant and irrelevant features from a datasets, thereby enhancing the classifier's performance. The Grey Wolf Optimizer (GWO) is a well-kno...

What factors enhance students' achievement? A machine learning and interpretable methods approach.

PloS one
Prior research on student achievement has typically examined isolated factors or bivariate correlations, failing to capture the complex interplay between learning behaviors, pedagogical environments, and instructional design. This study addresses the...

How does high temperature weather affect tourists' nature landscape perception and emotions? A machine learning analysis of Wuyishan City, China.

PloS one
Natural landscapes are crucial resources for enhancing visitor experiences in ecotourism destinations. Previous research indicates that high temperatures may impact tourists' perception of landscapes and emotions. Still, the potential value of natura...

Integrating multimodal imaging and peritumoral features for enhanced prostate cancer diagnosis: A machine learning approach.

PloS one
BACKGROUND: Prostate cancer is a common malignancy in men, and accurately distinguishing between benign and malignant nodules at an early stage is crucial for optimizing treatment. Multimodal imaging (such as ADC and T2) plays an important role in th...

Investigating the Key Trends in Applying Artificial Intelligence to Health Technologies: A Scoping Review.

PloS one
BACKGROUND: The use of Artificial Intelligence (AI) is exponentially rising in the healthcare sector. This change influences various domains of early identification, diagnosis, and treatment of diseases.