AIMC Topic: Cluster Analysis

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Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data.

Scientific reports
In recent years, the healthcare data system has expanded rapidly, allowing for the identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause of death in developed countries, often lea...

Dynamic Subcluster-Aware Network for Few-Shot Skin Disease Classification.

IEEE transactions on neural networks and learning systems
This article addresses the problem of few-shot skin disease classification by introducing a novel approach called the subcluster-aware network (SCAN) that enhances accuracy in diagnosing rare skin diseases. The key insight motivating the design of SC...

Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment.

Journal of translational medicine
INTRODUCTION: Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment de...

Multi-view clustering based on feature selection and semi-non-negative anchor graph factorization.

Neural networks : the official journal of the International Neural Network Society
Multi-view clustering has garnered significant attention due to its capacity to utilize information from multiple perspectives. The concept of anchor graph-based techniques was introduced to manage large-scale data better. However, current methods re...

Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine.

Scientific reports
The traditional diagnosis of chronic hepatitis C usually relies on liver biopsy. Diagnosing chronic hepatitis C based on serum indices provides a non-invasive way to determine the stage of chronic hepatitis C without liver biopsy. In this paper, we p...

NovoRank: Refinement for Peptide Sequencing Based on Spectral Clustering and Deep Learning.

Journal of proteome research
peptide sequencing is a valuable technique in mass-spectrometry-based proteomics, as it deduces peptide sequences directly from tandem mass spectra without relying on sequence databases. This database-independent method, however, relies solely on im...

Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach.

Substance use & misuse
BACKGROUND: Previous studies identified e-cigarette content on popular video and image-based social media platforms such as TikTok. While machine learning approaches have been increasingly used with text-based social media data, image-based analysis ...

Leveraging feature extraction and risk-based clustering for advanced fault diagnosis in equipment.

PloS one
In the contemporary manufacturing landscape, the advent of artificial intelligence and big data analytics has been a game-changer in enhancing product quality. Despite these advancements, their application in diagnosing failure probability and risk r...

Clustering and classification for dry bean feature imbalanced data.

Scientific reports
The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that c...