AIMC Topic: Cluster Analysis

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Aetiological clustering of newly diagnosed type 2 diabetes using machine learning: a retrospective cross-sectional study in Dubai, UAE.

BMJ open
OBJECTIVES: Type 2 diabetes (T2D) is a complex disease with a heterogeneous clinical presentation. Recently, five distinct clusters of T2D have been identified in the Emirati population of long-standing T2D with complications. This study aimed to val...

Data-driven cluster analysis identifies three clinical phenotypes in hemodialysis patients.

Renal failure
Clinical heterogeneity among hemodialysis patients necessitates precision medicine approaches transcending conventional single-parameter management. Through machine learning analysis of 1,207 maintenance hemodialysis patients, we developed a novel tw...

Uncovering age-specific subtypes of pediatric obesity and metabolic syndrome using machine learning algorithms.

Scientific reports
Identifying new subgroups among children and adolescents with obesity and metabolic syndrome requires advanced clustering techniques capable of analyzing complex multidimensional data. This study aimed to employ machine learning methods to enhance th...

Efficient Hybrid Hierarchical Clustering with Incremental Silhouette Score for Large, Noisy Datasets.

International journal of neural systems
This paper introduces a comprehensive framework for clustering analysis, centered on a novel incremental silhouette score calculation designed specifically for hierarchical clustering. This innovative method significantly reduces the computational co...

Efficient Vision Transformers for Ophthalmic Images Classification: A Comparative Study of Supervised, Semi-Supervised, and Unsupervised Learning Approaches.

Journal of medical systems
This study explored the integration of supervised, semi-supervised, and unsupervised learning strategies to classify ophthalmic images under label-scarce conditions. Given the high cost of annotations in medical imaging, the goal was to improve diagn...

Classifying complex multimorbidity using latent class analysis and machine learning to generate insights into clustering of mental and cardiometabolic conditions.

PloS one
Machine learning techniques earn higher accuracy and robustness in multimorbidity prediction at this moment in time. Among various forms of multimorbidity, complex multimorbidity, especially the intersection of cardiometabolic disorders and mental he...

Parkinson's disease severity clustering based on gait activity from mobile device.

Scientific reports
Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms, including gait impairments, which significantly affect patient mobility and quality of life. An accurate assessment of the severity of PD is crucial for clinica...

Identified endoplasmic reticulum stress-related molecular cluster and immune characterization in endometriosis.

Scientific reports
Endometriosis is a common disease among women of childbearing age, and endoplasmic reticulum stress (ERS), a response involved in regulating protein homeostasis, has been linked to its pathogenesis. To identify ERS-related hub genes, this study seque...

Deep structural clustering reveals hidden systematic biases in RNA sequencing data.

Genome research
RNA sequencing (RNA-seq) is a pivotal tool for transcriptomic analysis, providing comprehensive exploration of gene expression across diverse biological contexts. However, RNA-seq data are susceptible to various biases that can significantly compromi...

Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity.

BMC cardiovascular disorders
BACKGROUND: Obesity is a disease with high heterogeneity. Both overall obesity and central obesity are associated with increased risks of having cardio-metabolic co-morbidities. This study is aimed to examine the cardio-metabolic characteristics and ...